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daniyasiddiquiEditor’s Choice
Asked: 28/12/2025In:

scaling laws vs. efficiency-driven in ...

aiinnovationefficientaifutureofaimachinelearningscalinglawssustainableai
  1. daniyasiddiqui
    daniyasiddiqui Editor’s Choice

    Scaling Laws: A Key Aspect of AI Scaling laws identify a pattern found in current AI models: when you are scaling model size, the size of the training data, and computational capacity, there is smooth convergence. It is this principle that has driven most of the biggest successes in language, visionRead more

    Scaling Laws: A Key Aspect of AI

    Scaling laws identify a pattern found in current AI models:

    when you are scaling model size, the size of the training data, and computational capacity, there is smooth convergence. It is this principle that has driven most of the biggest successes in language, vision, and multi-modal AI.

    Large-scale models have the following advantages:

    • General knowledge of a wider scope
    • Effective reasoning and pattern recognition
    • Improved performance on various tasks

    Its appeal has been that it is simple to understand: “The more data you have and the more computing power you bring to the table, the better your results will be.” Organizations that had access to enormous infrastructure have been able to extend the frontiers of the potential for AI rather quickly.

    The Limits of Pure Scaling

    To better understand what

    1. Cost and Accessibility

    So, training very large-scale language models requires a huge amount of financial investment. Large-scale language models can only be trained with vastly expensive hardware.

    2. Energy and Sustainability

    Such large models are large energy consumers when trained and deployed. There are, thereby, environmental concerns being raised.

    3.Diminishing Returns

    When models become bigger, the benefits per additional computation become smaller, with every new gain costing even more than before.

    4. Deployment Constraints

    Most realistic domains, such as mobile, hospital, government, or edge computing, may not be able to support large models based on latency, cost, or privacy constraints.

    These challenges have encouraged a new vision of what is to come.

    What is Efficiency-Driven Innovation?

    Efficiency innovation aims at doing more with less. Rather than leaning on size, this innovation seeks ways to enhance how models are trained, designed, and deployed for maximum performance with minimal resources.

    Key strategies are:

    • Better architectures with reduced computational waste
    • Model compression, pruning, and quantization

    How knowledge distills from large models to smaller models

    • Models adapted to domains and tasks
    • Improved methods for training that require less data and computation.

    The aim is not only smaller models, but rather more functional, accessible, and deployable AI.

    The Increasing Importance of Efficiency

    1. Real-World

    The value of AI is not created in research settings but by systems that are used in healthcare, government services, businesses, and consumer products. These types of settings call for reliability, efficiency, explainability, and cost optimization.

    2. Democratization of AI

    Efficiency enables start-ups, the government, and smaller entities to develop very efficient AI because they would not require scaled infrastructure.

    3. Regulation and Trust

    Smaller models that are better understood can also be more auditable, explainable, and governable—a consideration that is becoming increasingly important with the rise of AI regulations internationally.

    4. Edge and On-Device AI

    Such applications as smart sensors, autonomous systems, and mobile assistants demand the use of ai models, which should be loowar on power and connectivity.

    Scaling vs. Efficiency: An Apparent Contradiction?

    The truth is, however, that neither scaling nor optimizing is going to be what the future of AI looks like: instead, it will be a combination of both.

    Big models will play an equally important part as:

    • General-purpose foundations
    • Identify Research Drivers for New Capabilities
    • Teachers for smaller models through distillation
    • On the other hand, the efficient models shall:

    Benefit Billions of Users

    • Industry solutions in the power industry
    • Make trusted and sustainable deployments possible

    This is also reflected in other technologies because big, centralized solutions are usually combined with locally optimized ones.

    The Future Looks Like This

    The next wave in the development process involves:

    • Increasingly fewer, but far better, large modelsteenagers
    • Rapid innovation in the area of efficiency, optimization, and specialization
    • Increasing importance given to cost, energy, and governance along with performance
    • Machine Learning Software intended to be incorporated within human activity streams instead of benchmarks

    Rather than focusing on how big, progress will be measured by usefulness, reliability, and impact.

    Conclusion

    Scaling laws enabled the current state of the art in AI, demonstrating the power of larger models to reveal the potential of intelligence. Innovation through efficiency will determine what the future holds, ensuring that this intelligence is meaningful, accessible, and sustainable. The future of AI models will be the integration of the best of both worlds: the ability of scaling to discover what is possible, and the ability of efficiency to make it impactful in the world.

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daniyasiddiquiEditor’s Choice
Asked: 28/12/2025In:

prompt engineering different from tra ...

aidevelopmentartificialintelligencegenerativeailargelanguagemodelsmachinelearningmodeltraining
  1. daniyasiddiqui
    daniyasiddiqui Editor’s Choice

    What Is Traditional Model Training Conventional training of models is essentially the development and optimization of an AI system by exposing it to data and optimizing its internal parameters accordingly. Here, the team of developers gathers data from various sources and labels it and then employsRead more

    What Is Traditional Model Training

    Conventional training of models is essentially the development and optimization of an AI system by exposing it to data and optimizing its internal parameters accordingly. Here, the team of developers gathers data from various sources and labels it and then employs algorithms that reduce an error by iterating numerous times.

    While training, the system will learn about the patterns from the data over a period of time. For instance, an email spam filter system will learn to categorize those emails by training thousands to millions of emails. If the system is performing poorly, engineers would require retraining the system using better data and/or algorithms.

    This process usually involves:

    • Huge amounts of quality data
    • High computing power (GPUs/TP
    • Time-consuming experimentation and validation
    • Machine learning knowledge for specialized applications

    After it is trained, it acts in a way that cannot be changed much until it is retrained again.

    What is Prompt Engineering?

    “Prompt Engineering” is basically designing and fine-tuning these input instructions or prompts to provide to a pre-trained model of AI technology, and specifically large language models to this point in our discussion, so as to produce better and more meaningful results from these models. The technique of prompt engineering operates at a purely interaction level and does not necessarily adjust weights.

    In general, the prompt may contain instructions, context, examples, constraints, and/or formatting aids. As an example, the difference between the question “summarize this text” and “summarize this text in simple language for a nonspecialist” influences the response to the question asked.

    Prompt engineering is based on:

    • Clear and well-structured instructions
    • Establishing Background and Defining Roles
    • Examples (few-shot prompting)
    • Iterative refinement by testing

    It doesn’t change the model itself, but the way we communicate with the model will be different.

    Key Points of Contrast between Prompt Engineering and Conventional Training

    1. Comparing Model Modification and Model Usage

    “Traditional training involves modifying the parameters of the model to optimize performance. Prompt engineering involves no modification of the model—only how to better utilize what knowledge already exists within it.”

    2. Data and Resource Requirements

    Model training involves extensive data, human labeling, and costly infrastructure. Contrast this with prompt design, which can be performed at low cost with minimal data and does not require training data.

    3. Speed and Flexibility

    Model training and retraining can take several days or weeks. Prompt engineering enables instant changes to the behavioral pattern through changes to the prompt and thus is highly adaptable and amenable to rapid experimentation.

    4. Skill Sets Involved

    “Traditional training involves special knowledge of statistics, optimization, and machine learning paradigms. Prompt engineering stresses the need for knowledge of the field, clarifying messages, and structuring instructions in a logical manner.”

    5. Scope of Control

    Training the model allows one to have a high, long-term degree of control over the performance of particular tasks. It allows one to have a high, surface-level degree of control over the performance of multiple tasks.

    Why Prompt Engineering has Emerged to be So Crucial

    The emergence of large general-purpose models has changed the dynamics for the application of AI in organizations. Instead of training models for different tasks, a team can utilize a single highly advanced model using the prompt method. The trend has greatly eased the adoption process and accelerated the pace of innovation,

    Additionally, “prompt engineering enables scaling through customization,” and various prompts may be used to customize outputs for “marketing, healthcare writing, educational content, customer service, or policy analysis,” through “the same model.”

    Shortcomings of Prompt Engineering

    Despite its power, there are some boundaries of prompt engineering. For example, neither prompt engineering nor any other method can teach the AI new information, remove deeply set biases, or function correctly all the time. Specialized or governed applications still need traditional or fine-tuning approaches.

    Conclusion

    At a very conceptual level, training a traditional model involves creating intelligence, whereas prompt engineering involves guiding this intelligence. Training modifies what a model knows, whereas prompt engineering modifies how a certain body of knowledge can be utilized. In this way, both of these aspects combine to constitute methodologies that create contrasting trajectories in AI development.

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daniyasiddiquiEditor’s Choice
Asked: 28/12/2025In:

multimodal AI models work

aimodelsartificialintelligencecomputervisiondeeplearningmachinelearningmultimodalai
  1. daniyasiddiqui
    daniyasiddiqui Editor’s Choice

    How Multi-Modal AI Models Function On a higher level, multimodal AI systems function on three integrated levels: 1. Modality-S First, every type of input, whether it is text, image, audio, or video, is passed through a unique encoder: Text is represented in numerical form to convey grammar and meaniRead more

    How Multi-Modal AI Models Function

    On a higher level, multimodal AI systems function on three integrated levels:

    1. Modality-S

    First, every type of input, whether it is text, image, audio, or video, is passed through a unique encoder:

    • Text is represented in numerical form to convey grammar and meaning.
    • Pictures are converted into visual properties like shapes, textures, and spatial arrangements.
    • The audio feature set includes tone, pitch, and timing.

    These are the types of encoders that take unprocessed data and turn it into mathematical representations that the model can process.

    2. Shared

    After encoding, the information from the various modalities is then projected or mapped to a common representation space. The model is able to connect concepts across representations.

    For instance:

    • The word “cat” is associated with pictures of cats.
    • The wail of the siren is closely associated with the picture of an ambulance or fire truck.
    • A medical report corresponds to the X-ray image of the condition.

    Such a shared space is essential to the model, as it allows the model to make connections between the meaning of different data types rather than simply handling them as separate inputs.

    3. Cross-Modal Reasoning and Generation

    The last stage of the process is cross-modal reasoning on the part of the model; hence, it uses multiple inputs to come up with outputs or decisions. It may involve:

    • Image question answering in natural language.
    • Production of video subtitles.
    • Comparing medical images with patient data.
    • The interpretation of oral instructions and generating pictorial or textual information.

    Instead, state-of-the-art multi-modal models utilize sophisticated attention mechanisms that highlight the relevant areas of the inputs during the process of reasoning.

    Importance of Multimodal AI Models

    1. They Reflect Real-World Complexity

    “The real world is multimodal.” This is because health and medical informatics, travel, and even human communication are all multimodal. This makes it easier for AI to handle information in such a way that it is processed in a way that human beings also do.

    2. Increased Accuracy and Contextual Understanding

    A single data source may be restrictive or inaccurate. Multimodal models utilize multiple inputs, making it less ambiguous and accurate than relying on one data source. For example, analyzing images and text information together is more accurate than analyzing only images or text information while diagnosing.

    3. More Natural Human AI Interaction

    Multimodal AIs allow more intuitive ways of communication, like talking while pointing at an object, as well as uploading an image file and then posing questions about it. As a result, AIs become more inclusive, user-friendly, and accessible, even to people who are not technologically savvy.

    4. Wider Industry Applications

    Multimodal models are creating a paradigm shift in the following:

    • Healthcare: Integration of lab results, images, and patient history for decision-making.
    • Learning is more effectively done by computer interaction, such as using text, pictures
    • Smart cities involve video interpretation, sensors, and reports to analyze traffic and security issues.
    • E-Governance: Integration of document processing, scanned inputs, voice recording, and dashboards to provide better services.

    5. Foundation for Advanced AI Capabilities

    Multimodal AI is only a stepping stone towards more complex models, such as autonomous agents, and decision-making systems in real time. Models which possess the ability to see, listen, read, and reason simultaneously are far closer to full-fledged intelligence as opposed to models based on single modalities.

    Issues and Concerns

    Although they promise much, multimodal models of AI remain difficult to develop and resource-heavy. They demand extensive data and alignment of the modalities, and robust protection against problems of bias and trust. Nevertheless, work continues to increase efficiency and trustworthiness.

    Conclusion

    Multimodal AI models are a major milestone in the field of artificial intelligence. Through the incorporation of various forms of knowledge in a single concept, these models bring AI a step closer to human-style perception and cognition. While the relevance of these models mostly revolves around their effectiveness, they play a crucial part in making AI systems more relevant and real-world.

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daniyasiddiquiEditor’s Choice
Asked: 26/12/2025In:

generative AI models

artificial intelligencedeep learningfine-tuningmachine learningpre-trainingtransfer learning
  1. daniyasiddiqui
    daniyasiddiqui Editor’s Choice

    Understanding the Two Model Types in Simple Terms Both generative and predictive AI models learn from data at the core. However, they are built for very different purposes. Generative AI models are designed to create content that had not existed prior to its creation. Predictive models are designedRead more

    Understanding the Two Model Types in Simple Terms

    Both generative and predictive AI models learn from data at the core. However, they are built for very different purposes.

    • Generative AI models are designed to create content that had not existed prior to its creation.
    • Predictive models are designed to forecast or classify outcomes based on existing data.

    Another simpler way of looking at this is:

    • Generative models generate something new.
    • Predictive models make decisions or estimates by deciding to do something or estimating something.

    What are Generative AI models?

    Generative AI models learn from the underlying patterns, structure, and relationships in data to produce realistic new outputs that resemble the data they have learned from.

    Instead of answering “What is likely to happen?”, they answer:

    • “What could be made possible?
    • What would be a realistic answer?
    • “How can I complete or extend this input?

    These models synthesize completely new information rather than simply retrieve already existing pieces.

    Common Examples of Generative AI

    • Text Generations and Conversational AI
    • Image and Video creation
    • Music and audio synthesis
    • Code generation
    • Document summarization, rewriting

    When you ask an AI to write an email for you, design a rough idea of the logo, or draft code, you are basically working with a generative model.

    What is Predictive Modeling?

    Predictive models rely on the analysis of available data to forecast an outcome or classification. They are trained on recognizing patterns that will generate a particular outcome.

    They are targeted at accuracy, consistency, and reliability, rather than creativity.

    Predictive models generally answer such questions as:

    • “Will this customer churn?”
    • Q: “Is this transaction fraudulent?
    • “What will sales be next month?”
    • “Does this image contain a tumor?”

    They do not create new content, but assess and decide based on learned correlations.

    Key Differences Explained Succinctly

    1. Output Type

    Generative models create new text, images, audio, or code. Predictive models output a label, score, probability, or numeric value.

    2. Aim

    Generative models aim at modeling the distribution of data and generating realistic samples. Predictive models aim at optimizing decision accuracy for a well-defined target.

    3. Creativity vs Precision

    Generative AI embraces variability and diversity, while predictive models are all about precision, reproducibility, and quantifiable performance.

    4. Assessment

    Evaluations of generative models are often subjective in nature-quality, coherence, usefulness-whereas predictive models are objectively evaluated using accuracy, precision, recall, and error rates.

    A Practical Example

    Let’s consider a sample insurance company.

    A generative model is able to:

    • Create draft summaries of claims
    • Generate customer responses
    • Explain policy details in plain language

    A predictive model can:

    • Predict claim fraud probability
    • Estimate claim settlement amounts
    • Risk classification of claims

    Both models use data, but they serve entirely different functions.

    How the Training Approach Differs

    • The generative models learn by trying to reconstruct data-sometimes instances of data, like an image, or parts of data, like the next word in a sentence.
    • Predictive models learn by mapping input features to a known output: predict yes/no, high/medium/low risk, or numeric value.
    • This difference in training objectives leads to very different behaviours in real-world systems.

    Why Generative AI is getting more attention

    Generative AI has gained much attention because it:

    • Allows for natural human–computer interaction
    • Automates content-heavy workflows
    • Creative, design, and communication support
    • Acts as an intelligence layer that is flexible across many tasks

    However, generative AI is mostly combined with predictive models that will make sure control, validation, and decision-making are in place.

    When Predictive Models Are Still Essential

    Predictive models remain fundamental when:

    • Decisions carry financial, legal, or medical consequences.
    • Outputs should be explainable and auditable.
    • It should operate consistently and deterministically.

    Compliance is strictly regulated. In many mature systems, generative models support humans, while predictive models make or confirm final decisions.

    Summary

    The end The generative AI models focus on the creation of new and meaningful content, while predictive models focus on outcome forecasting and decision-making. Generative models will bring flexibility and creativity, while predictive models will bring precision and reliability. Together, they provide the backbone of contemporary AI-driven systems, balancing innovation with control.

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daniyasiddiquiEditor’s Choice
Asked: 26/12/2025In:

pre-training vs fine-tuning

artificial intelligencedeep learningfine-tuningmachine learningpre-trainingtransfer learning
  1. daniyasiddiqui
    daniyasiddiqui Editor’s Choice

    “The Big Picture: Why Two Training Stages Exist” Nowadays, training of AI models is not done in one step. In most cases, two phases of learning take place. These two phases of learning are known as pre-training and fine-tuning. Both phases have different objectives. One can consider pre-training toRead more

    “The Big Picture: Why Two Training Stages Exist”

    Nowadays, training of AI models is not done in one step. In most cases, two phases of learning take place. These two phases of learning are known as pre-training and fine-tuning. Both phases have different objectives.

    One can consider pre-training to be general education, and fine-tuning to be job-specific training.

    Definition of Pre-Training

    This is the first and most computationally expensive phase of an AI system’s life cycle. In this phase, the system is trained on very large and diverse datasets so that it can infer general patterns about the world from them.

    For language models, it would mean learning:

    • Grammar and sentence structure
    • Lexical meaning relationships
    • Common facts

    Conversations and directions typically follow this pattern:

    Significantly, during pre-training, the training of the model does not focus on solving a particular task. Rather, it trains the model to predict either missing values or next values, such as the next word in an utterance, and in doing so, it acquires a general idea of language or data.

    This stage may require:

    • Large datasets (Terabytes of Data)
    • Strong GPUs or TPUs
    • Weeks or months of training time

    After the pre-training process, the result will be a general-purpose foundation model.

    Definition of Fine-Tuning

    Fine-tuning takes place after a pre-training process, aiming at adjusting a general model to a particular task, field, or behavior.

    Instead of having to learn from scratch, the model can begin with all of its pre-trained knowledge and then fine-tune its internal parameters ever so slightly using a far smaller dataset.

    • Fine-tuning is performed in
    • Enhance accuracy for a specific task
    • Assist alignment of the model’s output with business and ethical imperatives
    • Train for domain-specific language (medical, legal, financial, etc.)
    • Control tone, format, and/or response type

    For instance, a universal language understanding model may be trained to:

    • Answer medical questions more safely
    • Claims classification
    • Aid developers with code
    • Follow organizational policies

    This stage is quicker, more economical, and more controlled than the pre-training stage.

    Main Points Explained Clearly

    Conclusion

    General intelligence is cultivated using pre-training, while specialization in expert knowledge is achieved through

    Data

    It uses broad, unstructured, and diverse data for pre-training. Fine-tuning requires curated, labeled, or instruction-driven data.

    Cost and Effort

    The pre-training process involves very high costs and requires large AI labs. However, fine-tuning is relatively cheap and can be done by enterprises.

    Model Behavior

    After pre-training, it knows “a little about a lot.” Then, after fine-tuning, it knows “a lot about a little.”

    A Practical Analogy

    Think of a doctor.

    • “Pre-training” is medical school, wherein the doctor acquires education about anatomy, physiology, and general medicine.
    • Fine-tuning refers to specialization. It may include specialties such as cardiology or
    • Specialization is impossible without pre-training. Fine-tuning is necessary for the doctor to remain specialist.

    Why Fine-Tuning Is Significant for Real-World Systems

    Raw pre-trained models aren’t typically good enough in production contexts. There’s a benefit to fine-tuning a:

    • Decrease hallucinations in critical domains
    • Enhance consistency and reliability
    • synchronize results with legal stipulations
    • Adapt to local language, work flows, and terms

    It is even more critical within industries such as the medical sector, financial sectors, and government institutions that require accuracy and adherence.

    Fine-Tuning vs Prompt Engineering

    It should be noted that fine-tuning is not the same as prompt engineering.

    • Prompt engineering helps to steer the model’s conduct by providing more refined instructions, without modifying the model.
    • No, fine-tuning simply adjusts internal model parameters, making it behave in a predictable manner for all inputs.
    • Organizations begin their journey of machine learning tasks from prompt engineering to fine-tuning when greater control is needed.

    Whether a fine-tuning task can replace

    No. Fine-tuning is wholly reliant upon the knowledge derived during pre-trained models. There is no possibility of deriving general intelligence using fine-tuning with small data sets—it only molds and shapes what already exists or is already present.

    In Summary

    Pre-training represents the foundation of understanding in data and language that AI systems have, while fine-tuning allows them to apply this knowledge in task-, domain-, and expectation-specific ways. Both are essential for what constitutes the spine of the development of modern artificial intelligence.

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daniyasiddiquiEditor’s Choice
Asked: 26/12/2025In:

foundation models differ from task-sp ...

ai modelsartificial intelligencedeep learningfoundation modelsmachine learningmodel architecture
  1. daniyasiddiqui
    daniyasiddiqui Editor’s Choice

    The Meaning of Ground From a higher perspective, the distinction between foundation models and task-specific AI models is based on scope and purpose. In other words, foundation models constitute general intelligence engines, while task-specific models have a singular purpose accomplishing a single tRead more

    The Meaning of Ground

    From a higher perspective, the distinction between foundation models and task-specific AI models is based on scope and purpose. In other words, foundation models constitute general intelligence engines, while task-specific models have a singular purpose accomplishing a single task.

    Foundation models might be envisioned as highly educated generalists, while task-specific models might be considered specialists trained to serve only one role in society.

    What Are Foundation Models?

    Foundation models are large-scale AI models. They require vast and diverse data sets. These data sets involve various domains like language, images, code, audio, and structure. Foundation models are not trained on a fixed task. They learn universal patterns and then convert them into task-specific models.

    Once trained, the same foundation model can be applied to the following tasks:

    • Text generation
    • Question Answering
    • Summar
    • Translation
    • Image understanding
    • Code assistance
    • Data analysis

    “These models are ‘ foundational’ because a variety of applications are built upon these models using a prompt, fine-tuning, or a light-weight adapter. ”

    What Are Task-Specific AI Models?

    The models are trained using a specific, narrow objective. Models are built, trained, and tested based on one specific, narrowly defined task.

    These include:

    • An email spam classifier
    • A face recognition system.
    • Medical Image Tumor Detector
    • A credit default prediction model
    • A speech-to-text engine for a given language

    These models are not meant for generalization for a domain other than their use case. For any domain other than their trained tasks, their performance abruptly deteriorates.

    Differences Explained in Simple Terms

    1. Scope of Intelligence

    Foundation models generalize the learned knowledge and can perform a large number of tasks without needing additional training. Task-specific models specialize in a single task or a single specific function and cannot be readily adapted or applied to other tasks.

    2. Training Methodology

    Foundation models are trained once on large datasets and are computationally intensive. Task-specific models are trained on smaller datasets but are specific to the task they are meant to serve.

    3. Reusability & Adapt

    An existing foundation model can be easily applied to different teams, departments, or industries. In general, a task-specific model will have to be recreated or retrained for each new task.

    4. Cost and Infrastructure

    Nonetheless, training a foundation model is costly but efficient in the use of models since they accomplish multiple tasks. Training task-specific models is rather inexpensive but turns costly if multiple models have to be developed.

    5. Performance Characteristics

    Task-specific models usually perform better than foundation models on a specific task. But for numerous tasks, foundation models provide “good enough” solutions that are much more desirable in practical systems.

    Actual Example

    Consider a hospital network.

    A foundation model can:

    1. Generate

    • Summarize patient files
    • Respond to questions from clinicians.
    • Create discharge summaries
    • Translation of medical records
    • Provide help regarding coding and billing questions

    Task-specific models could:

    • Pneumonia identification from chest X-rays alone
    • Both are important, but they are quite different.

    Why Foundation Models Are Gaining Popularity

    Organisations have begun to favor foundation models because they:

    • Cut the need for handling scores of different models
    • Accelerate adoption of AI solutions by other departments in
    • Allow fast experimentation with prompts over having to retrain
    • Support multimodal workflows (text + image + data combined)

    This has particular importance in business, healthcare, finance, and e-governance applications, which need to adapt to changing demands.

    Even when task-specific models are still useful

    Although foundation models have become increasingly popular, task-specific models continue to be very important for:

    • Approvals need to be deterministic
    • Very high accuracy is required for one task
    • Latency and compute are very constrained.
    • The job deals with sensitive or controlled data

    In principle many existing mature systems would employ foundation models for general intelligence and task-specific models for critical decision-making.

    In Summary

    Foundation models add the ingredient of width or generic capability with scalability and adaptability. Task-specific models add the ingredient of depth or focused capability with efficiency. Contemporary AI models and applications increasingly incorporate the best aspects of the first two models.

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daniyasiddiquiEditor’s Choice
Asked: 23/12/2025In:

you reduce latency in AI-powered appl ...

aioptimizationedgecomputinginference #latencymachinelearningmodeloptimization
  1. daniyasiddiqui
    daniyasiddiqui Editor’s Choice

    1. First, Understand Where Latency Comes From Before reducing latency, it's important to understand why AI systems feel slow. Most delays come from a combination of: Network calls to AI APIs Large model inference time Long or badly structured utterances Repetitive computation for similar requests BaRead more

    1. First, Understand Where Latency Comes From

    Before reducing latency, it’s important to understand why AI systems feel slow. Most delays come from a combination of:

    • Network calls to AI APIs
    • Large model inference time
    • Long or badly structured utterances
    • Repetitive computation for similar requests
    • Back end bottlenecks: databases, services, authentication

    Simplified: The AI is doing too much work, too often, or too far from the user.

    2. Refine the Prompt: Less is Better- Say It Better

    One of the causes for latency that is usually overlooked is too-long prompts.

    Why this matters:

    • AI models process text one token at a time. The longer the input, the longer the processing time and the greater the cost.

    Practical improvements:

    • Remove from the text unnecessary instructions or repeated context.
    • Avoid sending entire documents when summaries will do
    • Keep system prompts short and focused.
    • Structure prompts instead of wordiness.

    Well-written prompts are improving the performance to enhance speed but also increasing the quality of the output.

    3. Choose the Right Model for the Job

    Not every task requires the largest or most powerful AI model.

    Human analogy:

    • You do not use a supercomputer to calculate a grocery bill.

    Practical approach:

    • Stick to smaller or faster models for more mundane tasks.
    • Use large models only if complex reasoning or creative tasks are required.
    • Use task-specific models where possible (classification, extraction, summarization)

    This can turn out to be a very significant response time reducer on its own.

    4. Use Caching: Don’t Answer the Same Question Twice

    Among all the different latency reduction techniques, caching is one of the most effective.

    Overview: How it works:

    • Store the AI’s response for similar or identical user questions and reuse rather than regenerate.

    Where caching helps:

    • Frequently Asked Questions
    • Static explanations
    • Policy/guideline responses
    • Repeated insights into the dashboard

    Result:

    • There are immediate responses.
    • Lower AI costs
    • Reduced system load

    From the user’s standpoint, the whole system is now “faster and smarter”.

    5. Streaming Responses for Better User Experience

    Even though the complete response takes time to come out, sending partial output streaming out makes the system seem quicker.

    Why this matters:

    • Basically, the users like to see that something is being done without just hanging there silently.

    Example:

    • Chatbots typing responses line after line.
    • Dashboards loading insights progressively

    This does not save computation time, but it saves perceived latency, which is sometimes just as good.

    6. Using Retrieval-Augmented Generation: It is best used judiciously.

    RAG combines AI with external data sources. Powerful but may introduce delays, if poorly designed.

    In reducing latency for RAG:

    • Limit the number retrieved.
    • Use efficient vector databases
    • Pre-index and pre-embed content
    • Filter results prior to sending them to the model.

    So, instead of sending in “everything,” send in only what the model needs.

    7. Parallelize and Asynchronize Backend Operations

    • AI calls should not block the whole application.
    • Practical Strategies
    • Run AI calls asynchronously
    • Parallel database queries and API calls
    • Decouple the AI processing from the rendering of the UI.

    This ensures that users aren’t waiting on a number of systems to complete a process sequentially.

    8. Minimize delays in networks and infrastructures

    Sometimes the AI is fast-but the system around it is slow.

    Common repairs:

    • Host services closer to users, regional hosting of AI services
    • Optimize API gateways
    • Minimize wasteful authentication round-trips
    • Use persistent connections

    Tuning of infrastructure often yields hidden and important benefits in performance.

    9. Preprocessing and Precomputation

    In many applications, the insights being generated do not have to be in real time.

    • Examples:
    • Analytics health reports on a daily basis
    • Summary of financial risks
    • Government scheme performance dashboards

    Generating these ahead of time enables the application to just serve the results instantly when requested.

    10. Continuous Monitoring, Measurement, and Improvement

    Optimization of latency is not a one-time process.

    • What Teams Monitor
    • Average response time
    • Peak-time performance
    • Slowest user journeys
    • AI Inference Time

    Real improvements come from continuous tuning based on real usage patterns, not assumptions.

    • Why This Matters So Much

    From the user’s perspective:

    • Fast systems feel intelligent
    • Slow systems feel unreliable

    From the perspective of an organization:

    • Lower latency translates to lower cost.
    • Greater performance leads to better adoption
    • Smarter, Faster Decisions Improve Outcomes

    Indeed, be it a waiting doctor for insights, a citizen tracking an application, or even a customer checking on a transaction, speed has a direct bearing on trust.

    In Simple Terms

    This means, by reducing latency, AI-powered applications can:

    • Asking the AI only what is required.
    • Choosing the Model

    Eliminating redundant work Designing smarter backend flows Make the system feel responsive, even when work is ongoing

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daniyasiddiquiEditor’s Choice
Asked: 23/12/2025In:

AI being used in healthcare, finance, ...

aiapplicationsdigitalgovernmentegovernancefinanceaihealthcareaismartsystems
  1. daniyasiddiqui
    daniyasiddiqui Editor’s Choice

    1. Diagnosis and Medical Imaging The AI analyzes X-rays, CT scans, MRIs, and pathology slides for the diagnosis of diseases such as cancer, tuberculosis, and neurological disorders. Flag abnormalities early Improve diagnostic accuracy: Reduce the To support doctors in large-volume hospitals This isRead more

    1. Diagnosis and Medical Imaging

    The AI analyzes X-rays, CT scans, MRIs, and pathology slides for the diagnosis of diseases such as cancer, tuberculosis, and neurological disorders.

    • Flag abnormalities early
    • Improve diagnostic accuracy: Reduce the
    • To support doctors in large-volume hospitals

    This is even more precious in an area where qualified physicians are few.

    2. Predictive & Preventive Healthcare

    The AI system evaluates patient records, laboratory results, and lifestyle information for the following purposes:

    • Predict Disease Risk (Diabetes/Heart Disease)
    • Early recognition of high-risk patients
    • Encourage preventative approaches over emergency care

    The medical industry is gradually moving from a culture of ‘treat after illness’ to ‘predict before illness.’

    3. Hospital Operations and Administration

    AI can already now be found in the background of many tasks such as:

    • Predicting Bed Occupancy
    • Staff scheduling
    • “Inventory Management” is a module of
    • Automated claims processing

    These ensure reduced human labor and allow healthcare providers to give attention to patients.

    4. Telemedicine and Virtual Health Assistants

    Chatbots assisted by artificial intelligence are helpful:

    • Book Appointments
    • Learn Symptoms
    • Get drug reminders
    • Follow post-discharge instructions

    Additionally, for people in rural and remote areas, it is an improvement in access for guidance on basic healthcare needs.

    5. Fraud Detection and Risk Management

    AI systems track real-time transactions on a scale of millions to:

    • Identify unusual purchase behavior
    • Prevent fraudulent transactions immediately
    • Minimize false positives, as in rule-based systems
    • It safeguards both customers and financial institutions.

    6. Credit Scoring and Loan Decisions

    Conventional credit scoring involves limited data. It is expanded by AI using information from:

    • Transaction behavior
    • Repayment patterns
    • Cash flow trends

    This allows:

    • Quick loan approvals
    • Credit accessibility for people with limited credit experience
    • Enhanced risk evaluation
    • Risk evaluation is one

    7. Algorithmic Trading and Market Analysis

    The AI models assess market trends, news sentiment, and historical information on:

    • Execute trades at high speeds
    • Minimize human bias when making decisions
    • Optimize Portfolio Performance

    Though strategies are determined by human initiative, implementation as well as data processing is done by AI.

    8. Customer Service and Personal Finance

    Artificial intelligence assistants assist customers in the following ways:

    • Account queries
    • Payment issues
    • Investment Insights
    • Budgeting suggestions

    This increases service availability and cuts the pressure on call centers.
    Copyright by journalsp

    9. Automated Public Service Delivery

    AI makes the following processes easier for governments:

    • Applications
    • Verifications
    • Griev
    • Eligibility checks

    This eliminates delays, paperwork, and the need for human intervention.

    10. Data-Driven Policy and Decision-M

    Data is being generated on an enormous scale in various sectors like the healthcare and education sectors, and also in the transportation and welfare sectors. AI is able

    • Identify gaps in service delivery
    • Measure Scheme Performance
    • Encourage evidence-based policy development

    Artificial Intelligence-driven dashboards make it possible for officials to react accordingly.

    11. Detecting Frauds in Welfare Schemes

    AI is employed in:

    • Identify Duplicate Beneficiaries
    • Determining counterfeit claims
    • Prevent fund leakage

    This ensures the targeted group receives the benefits and the public funds are safeguarded.

    12. Citizen Interaction and Accessibility

    AI-based chatbots and voice assistants assist residents in the following ways:

    • Provide access to local language information
    • Applications tracking
    • Get immediate answers without physically coming to our offices

    This is an upgrade for inclusivity, particularly for the elderly.

    Common Benefits Across All Three Sectors

    Although there may be different applications in different places, the same high-impact results are achieved by all:

    • Faster decision-making
    • Decreased human error
    • Cost Optimization
    • More effective use of resources
    • Enhanced user experience

    Most notably, AI enhances human potential, rather than replacing it.

    The Human Reality with AI Implementation

    Although there are efficiency gains associated with AI, there are important implications associated with it as well:

    • Data privacy & security
    • Privacy refers to
    • Bias and fairness
      Regardless,
    • Transparency of decision-making
    • Ethical and regulatory compliance

    For a successful adoption of AI, there is a need to strike a proper balance between technology

    In Simple Words

    • Healthcare: incorporates AI technology in predicting diseases, assisting physicians, and taking care of patients
    • Finance: leverages AI for securing funds, risk management, and personalizing services
    • E-Governance: makes use of AI to provide faster, just, and transparent public services
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daniyasiddiquiEditor’s Choice
Asked: 23/12/2025In:

few-shot, one-shot, and zero-shot pro ...

aiconceptschatgptfewshotllmsoneshotzeroshot
  1. daniyasiddiqui
    daniyasiddiqui Editor’s Choice

    1. Zero Shot Prompting: “Just Do It In zero-shot prompting, the AI will be provided with only the instruction and without any example at all. It is expected that the model will be completely dependent on its previous training knowledge. What it looks like: Simply tell the AI what you want. Example:Read more

    1. Zero Shot Prompting: “Just Do It

    In zero-shot prompting, the AI will be provided with only the instruction and without any example at all. It is expected that the model will be completely dependent on its previous training knowledge.

    What it looks like:

    • Simply tell the AI what you want.

    Example:

    • “Classify the email below as spam or not spam.”
    • There are no examples given. The computer uses what it already knows about spam patterns to make decisions.

    When zero-shot learning is most helpful:

    • “The task is simple or common” is one example of
    • The instruction is clear and unequivocal
    • You expect quick answers with small inputs.
    • Costs and latency are considerations
    • Limitations
    • Results can vary depending on the nature of the activity, especially when it is
    • Less reliable for domain-specific or complex tasks
    • “AI can interpret a task differently than its human author intended”

    In other words, zero-shot is like saying, “That’s the job, now go,” to a new employee.

    “2. One-Shot Prompting: “Here’s

    In one-shot prompting, you provide an example of what you would like the AI to produce. This example example helps to align the AI’s understanding of what you are trying to get across.

    What it looks like:

    step 1.

    you give one example. Then comes the actual question.

    • # Example
    • “Example
    • Email: You have won a free prize!
      → Spam

    This can be considered as:

    • “Your meeting is scheduled for tomorrow.”
    • This example alone helps to explain the structure and reasoning required.

    One-shot is good when:

    • There is more than one way of interpreting this task
    • You want to control format or tone
    • “The zero-shot results were inconsistent”
    • You want greater accuracy without a lengthy prompt

    Limitations

    • One Example May Still Not Include Edge Cases
    • Marginally higher usage than zero shot

    Step 2.

    • Whether quality is important or not also depends on how good an example is
      While quality is
    • One shot prompting is like: “Here’s one sample, do it like this.” Examples are: 1. When

    3. Few-Shot Prompting: “Learn from These

    Few-shot prompting involves several examples prior to the task at hand. Examples aid the AI in pattern recognition to enable pattern application.

    What it looks like:

    • There are various pairs of input and output that you provide, followed by asking the model to continue.

    Example:

    Example 1:

    • Review: ‘Excellent product!’ → Positive

    Example 2:

    • Explanation: ‘Very disappointing experience.’ → Negative

    Now classify:

    • “The service was okay, not great.”
    • The AI infers sentiment patterns based on the examples.

    When few-shot is best:

    • The problem is complex or domain-specific
    • There has to be strict precision in the output format being followed
    • You require more reliability and consistencies
    • You want the machine to trace a specific path of reasoning

    Limitations

    • Longer prompts are associated with higher costs as well as higher latency
    • There are too many examples to list them all out
    • Not scalable in the case of large or dynamic knowledge bases

    Few-shot prompting is analogous to teaching a person several example solutions before assigning them an exercise.

    How This Is Used in Real Systems

    In real-world AI applications:

    Zero-shot is common for chatbots on general questions

    One-shot: When formatting or tone issues are involved few shot is employed in business operations, assessments, and output. Frequently, the team begins with zero-shot learning and increases the data gradually until the outcomes are satisfactory.

    Key Takeaways

    Zero-shot example: “Do this task
    One-shot: “Here’s one example, do it like this.
    Few-shot: “Here are multiple examples follow the pattern.”

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daniyasiddiquiEditor’s Choice
Asked: 23/12/2025In:

prompts, user prompts, and guardrails

aiaiconceptsartificialintelligencechatgptllmspromptengineering
  1. daniyasiddiqui
    daniyasiddiqui Editor’s Choice

    1. System The above discussed the role to be performed, the rules to be followed, and the personality of the AI. A system prompt is an invisible instruction given to the AI before any user interaction starts. It defines who the AI is, how it shall behave, and what are its boundaries. Direct end userRead more

    1. System The above discussed the role to be performed, the rules to be followed, and the personality of the AI.

    A system prompt is an invisible instruction given to the AI before any user interaction starts. It defines who the AI is, how it shall behave, and what are its boundaries. Direct end users don’t usually see system prompts; however, they strongly influence every response.

    What do system prompts:

    • Set the tone and style (formal, friendly, concise, explanatory)
    • Establish behavioral guidelines: do not give legal advice; do not create harmful content.
    • Prioritize accuracy, safety, or compliance

    Simple example:

    • “You are a healthcare assistant. Provide information that is factually correct and in a non-technical language. Do not diagnose or prescribe medical treatment.
    • In this way, from now on, the AI can color each response with this point of view, despite attempts by users to push it in another direction.

    Why System Prompts are important:

    • They ensure consistency in the various conversations.
    • They prevent misuse of the AI.
    • They align the AI with business, legal, or ethical requirements

    The responses of the AI without system prompts would be general and uncontrolled.

    2. User Prompts: The actual question or instructions

    A user prompt is the input provided by the user during the conversation. This is what most people think of when they “talk to AI.”

    What user prompts do:

    • Tell the AI what to do.
    • Provide background, context or constraints
    • Influence the depth and direction of the response.

    Examples of user prompts:

    • “Explain cloud computing in simple terms.”
    • Letter: Requesting two days leave.
    • Overview: Summarize this report in 200 words.

    User prompts may be:

    • Short and to the point.
    • Elaborate and organized
    • Explanatory or chatty

    Why user prompts matter:

    • Clear prompts produce better outputs.
    • Poorly phrased questions are mostly the reason for getting unclear or incomplete answers.
    • That same AI, depending on how the prompt is framed, can give very different responses.

    That is why prompt clarity is often more important than the technical complexity of a task.

    3. Guardrails: Safety, Control, and Compliance Mechanisms

    Guardrails are the safety mechanisms that control what the AI can and cannot do, regardless of the system or user prompts. They act like policy enforcement layers.

    What guardrails do:

    • Prevent harmful, illegal or unethical answers
    • Enforce compliance according to regulatory and organizational requirements.
    • Block or filter sensitive data exposure
    • Detection and prevention of abuse, such as prompt injection attacks

    Examples of guardrails in practice:

    • Refusing to generate hate speech or explicit content
    • Avoid financial or medical advice without disclaimers
    • Preventing access to confidential or personal data.

    Stopping the AI from following malicious instructions even when insisted upon by the user.

    Types of guardrails:

    • Topic guardrails: what topics are in and what are out
    • Behavioural guardrails: How the AI responds
    • Security guardrails can include anything from preventing manipulation to blocking data leaks.
    • Compliance guardrails: GDPR, DPDP Act, HIPAA, etc.

    Guardrails work in real-time and continuously override system and user prompts when necessary.

    How They Work Together: Real-World View

    You can think of the interaction like this:

    • System prompt → Sets career position and guidelines.
    • User prompt → Provides the task
    • Guardrails → Ensure nothing unsafe or non-compliant happens

    Practical example:

    • System prompt: “You are a bank customer support assistant.
    • User prompt: “Tell me how to bypass KYC.”
    • guardrails Block the request and respond with a safe alternative

    Even if the user directly requests it, guardrails prevent the AI from carrying out the action.

    Why This Matters in Real Applications

    These three layers are very important in enterprise, government, and healthcare systems because:

    • They ensure trustworthy AI
    • They reduce legal and reputational risk.
    • They enhance the user experience by relevance and safety of response.

    They allow organizations to customize the behavior of AI without retraining models.

    Summary in Lamen Terms

    • System prompts are what define who the AI is, and how it shall behave.
    • User prompts define what the AI is asked to do.

    Guardrails provide clear boundaries within which the AI will keep it safe, ethical, and compliant. Working together, they transform a powerful, general AI model into a controlled, reliable, and responsible digital assistant fit for real-world application.

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