prompt engineering different from tra ...
1. Iran Sees the Protests as an Existential Threat Iran’s leadership frames the current wave of protests not merely as demonstrations, but as a direct challenge to the authority and stability of the Islamic Republic. Officials including the judiciary have publicly described many detainees as “rioterRead more
1. Iran Sees the Protests as an Existential Threat
Iran’s leadership frames the current wave of protests not merely as demonstrations, but as a direct challenge to the authority and stability of the Islamic Republic. Officials including the judiciary have publicly described many detainees as “rioters,” “terrorists,” or even “enemies of God” under Iranian law, which carries the death penalty. This characterization is significant because charges like moharebeh (“waging war against God”) and corruption on Earth are among the most severe in Iran’s penal code and can justify expedited procedures and capital punishment.
Fast-tracking trials and executions, from the regime’s perspective, is intended to crush dissent quickly and signal to the population that any large-scale challenge to state power will be met with overwhelming force.
2. The Judiciary’s Own Rationale: Speed to Maintain Order
Iran’s top judicial officials have explicitly stated that delays in prosecuting protesters would diminish the “impact” of judicial action. The head of the judiciary, Gholamhossein Mohseni-Ejei, emphasized that addressing cases promptly is essential in his view for justice to serve its purpose and deter further unrest. That official discourse is used internally to justify accelerated case handling and harsh sentencing.
3. A Response to Widespread Unrest and State Violence
The current protests are among the largest and most sustained anti-government demonstrations in Iran in decades, sparked by deep economic grievances and evolving into broader demands for political change. Security forces have killed large numbers of civilians in clashes with demonstrators, and tens of thousands of people have been arrested. The scale of unrest combined with efforts by the government to maintain control underpins the judiciary’s push to conclude cases rapidly and impose severe penalties, including death sentences, to create a chilling effect.
4. International Pressure and Internal Messaging
Iran’s leadership is operating under intense international scrutiny and pressure, including warnings from the United States and concerns from human rights bodies. Rather than softening its stance, the judiciary’s signaling of fast trials and executions appears partly intended to display resolve domestically and to international audiences that it will not bow to external demands. Officials often justify this approach by accusing foreign powers of inciting or supporting unrest.
5. Human Rights Concerns About Due Process
Human rights organizations have long documented that Iran’s use of fast-track or “summary” trials in politically charged cases often comes at the expense of basic legal protections. Reports from earlier protest waves show that defendants have been denied meaningful access to lawyers, subjected to forced confessions, and convicted after proceedings that fall far short of international fair-trial standards. This historical pattern intensifies global concern about the current situation.
6. Symbolism and Deterrence in a Climate of Fear
In legal and symbolic terms, swift judgments and executions serve multiple functions:
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Deterrence: Harsh and quick punishments are intended to deter others from participating in protests.
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Reassertion of Authority: It shows the regime is unwilling to tolerate challenges to its rule.
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Internal Messaging: Within governmental, judicial, and security structures, such measures reinforce discipline and loyalty.
Taken together, these elements demonstrate that fast-tracking trials and executions for detained protesters is part of a broader strategy by Iran’s leadership to maintain control and intimidate opposition amid one of the most volatile periods in its modern history.
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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:
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:
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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