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daniyasiddiquiImage-Explained
Asked: 19/10/2025In: Technology

How do we craft effective prompts and evaluate model output?

we craft effective prompts and evalua ...

ai accuracyai output evaluationeffective promptingnatural languageprompt designprompt engineering
  1. daniyasiddiqui
    daniyasiddiqui Image-Explained
    Added an answer on 19/10/2025 at 3:25 pm

     1. Approach Prompting as a Discussion Instead of a Direct Command Suppose you have a very intelligent but word-literal intern to work with. If you command them, "Write about health," you are most likely going to get a 500-word essay that will do or not do what you wanted to get done. But if you comRead more

     1. Approach Prompting as a Discussion Instead of a Direct Command

    Suppose you have a very intelligent but word-literal intern to work with. If you command them,

    “Write about health,”
    you are most likely going to get a 500-word essay that will do or not do what you wanted to get done.

    But if you command them,

    • “150-word doctors’ blog on how AI is helping diagnose heart disease, in simple English and one real-life example,”you’ve demonstrated guidance, context, tone, and reasoning.
    • That’s how AI models function also — they are not telepathic, but rule-following.
    • A good prompt is one that forbids vagueness and gives the model a “mental image” of what you require.

    2. Structure Matters: Take the 3C Rule — Context, Clarity, and Constraints.

    1️⃣ Context – Tell the model who it is and what it’s doing.

    • “You are a senior content writer for a healthcare startup…”
    • “You are a data analyst who is analyzing hospital performance metrics…”
    • This provides the task and allows the model to align tone, vocabulary, and priority.

    2️⃣ Clarity – State the objective clearly.

    • “Explain the benefits of preventive care to rural patients in basic Hindi.”
    • Avoid general words like “good,” “nice,” or “professional.” Use specifics.

    3️⃣ Constraints – Place boundaries (length, format, tone, or illustrations).

    • “Be brief in bullets, 150 words or less, and end with an action step.”
    • Constraints restrict the output — similar to sketching the boundaries for a painting before filling it in.

    3. Use “Few-Shot” or “Example-Based” Prompts

    AI models learn from patterns of examples. Let them see what you want, and they will get it in a jiffy.

    Example 1: Bad Prompt

    • “Write a feedback message for a hospital.”

    Example 2: Good Prompt

    “See an example of a good feedback message:

    • ‘The City Hospital staff were very supportive and ensured my mother was comfortable. Thanks!’
    • Make a similar feedback message for Sunshine Hospital in which the patient was contented with timely diagnosis and sanitation of the rooms.”

    This technique — few-shot prompting — uses one or several examples to prompt the style and tone of the model.

    4. Chain-of-Thought Prompts (Reveal Your Step-by-Step Thinking)

    For longer reasoning or logical responses, require the model to think step by step.

    Instead of saying:

    • “What is the optimal treatment for diabetes?”

    Write:

    • “Step-by-step describe how physicians make optimal treatment decisions in a Type-2 diabetic patient from diagnosis through medication and conclude with lifestyle advice.
    • This is called “chain-of-thought prompting.” It encourages the model to show its reasoning process, leading to more transparent and correct answers.

     5. Use Role and Perspective Prompts

    You can completely revolutionize answers by adding a persona or perspective.

    Prompt Style\tExample\tOutput Style
    Teacher
    “Describe quantum computing in terms you would use to explain it to a 10-year-old.”
    Clear, instructional
    Analyst
    “Write a comparison of the advantages and disadvantages of having Llama 3 process medical information.”
    Formal, fact-oriented
    Storyteller
    “Briefly tell a fable about an AI developing empathy.”
    Creative, storytelling
    Critic
    “Evaluate this blog post and make suggestions for improvement.”
    Analytical, constructive

    By giving the model something to do, you give it a “voice” and behavior reference point — what it spits out is more intelligible and easier to predict.

    6. Model Output Evaluation — Don’t Just Read, Judge

    • You don’t have a good prompt unless you also judge the output sensibly.
    • Here’s how people can evaluate AI answers other than “good” or “bad.”

    A. Relevance

    Does the response actually answer the question or get lost?

    •  Good: Straightforward on-topic description
    •  Bad: Unrelated factoid with no relevance to your goal

    B. Accuracy

    • Verify accuracy of facts — especially for numbers, citations, or statements.
    • Computer systems tend to “hallucinate” (adamantly generating falsehoods), so double-check crucial things.

    C. Depth and Reasoning

    Is it merely summarizing facts, or does it go further and say why something happens?

    Ask yourself:

    • “Tell me why this conclusion holds.”
    • “Can you provide a counter-argument?”

    D. Style and Tone

    • Is it written in your target market?
    • A well-written technical abstract for physicians might be impenetrable to the general public, and conversely.

    E. Completeness

    • Does it convey everything that you wanted to know?
    • If you asked for a table, insights, and conclusion — did it provide all three?

    7. Iteration Is the Secret Sauce

    No one — not even experts — gets the ideal prompt the first time.

    Feel free to ask as you would snap a photo: you adjust the focus, lighting, and view until it is just right.

    If an answer falls short:

    • Read back your prompt: was it unclear?
    • Tweak context: “Explain in fewer words” or “Provide sources of data.”
    • Specify format: “Display in a markdown table” or “Write out in bullet points.”
    • Adjust temperature: down for detail, up for creativity.

    AI is your co-builder assistant — you craft, it fine-tunes.

     8. Use Evaluation Loops for Automation (Developer Tip)

    Evaluating output automatically by:

    • Constructing test queries and measuring performance (BLEU, ROUGE, or cosine similarity).
    • Utilizing human feedback (ranking responses).
    • Creating scoring rubrics: e.g., 0–5 for correctness, clarity, creativity, etc.

    This facilitates model tuning or automated quality checks in production lines.

     9. The Human Touch Still Matters

    You use AI to generate content, but you add judgment, feeling, and ethics to it.

    Example to generate health copy:

    • You determine what’s sensitive to expose.
    • You command tone and empathy.
    • You choose to communicate what’s true, right, and responsible.

    AI is the tool; you’re the writer and meaning steward.

    A good prompt is technically correct only — it’s humanly empathetic.

     10. In Short — Prompting Is Like Gardening

    You plant a seed (the prompt), water it (context and structure), prune it (edit and assess), and let it grow into something concrete (the end result).

    • “AI reacts to clarity as light reacts to a mirror — the better the beam, the better the reflection.”
    • So write with purpose, futz with persistence, and edit with awe.
    • That’s how you transition from “writing with AI” to writing with AI.
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