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Prompt Engineering

What is a Prompt?

  • Structuring inputs (questions or commands) to guide an AI model to produce accurate and relevant output.
  • Helps reduce hallucinations, bias, and vague answers.
  • Useful for anyone working with AI: developers, researchers, content creators, and more.

Types of Prompts

There are several common types of prompts used with AI language models:

  • Zero-shot Prompting: Give the model a task without examples.
  • Few-shot Prompting: Show the model a few examples to guide its responses.
  • Chain-of-thought Prompting: Ask the model to reason step-by-step.
  • Instruction-based Prompting: Provide clear, structured instructions.
  • Role-based Prompting: Assign a role or persona to the model.
  • Contextual or Dynamic Prompting: Insert relevant background/context before the question.
  • Multi-turn Prompting: Build context across a conversation.
  • Prompt Chaining (Modular Prompting): Break tasks into smaller steps, using outputs as new inputs.

For detailed definitions and examples, see Types of Prompts.

Why Prompt Engineering Matters

Prompt engineering means crafting prompts to guide AI models. The main goals:

  • Improve accuracy and relevance of responses
  • Minimize hallucinations (false information)
  • Reduce ambiguity in instructions
  • Encourage creative or insightful results when needed

Principles of Good Prompt Engineering

Use these principles for better prompts:

  • Clarity: Be specific and unambiguous.
  • Context: Give background if needed.
  • Structure: Use lists or clear formatting.
  • Examples: Show what you want the AI to produce.
  • Role Play: Ask the AI to act as an expert or persona if helpful.

Tips for Better Prompts

  1. Be clear and specific

    Bad: "Tell me about plants."
    Good: "What are the benefits of growing lettuce in a hydroponic NFT system indoors?"

    Include relevant context, constraints, or goals in your prompt.


  2. Provide a role or perspective (optional but powerful)

    "Act as a data scientist.
    Can you explain how to fine-tune a transformer model using PyTorch?"

    This helps the AI simulate a more accurate tone or specialist mindset.


  3. Break complex queries into steps

    "I want to write a research paper.
    First, help me outline the key sections, then we can work on each section together."

    Chunking improves focus and minimizes confusion.


  4. Set the format you want

    "Give me a bullet-point summary."
    "Write it as a table with columns: Feature | Description | Pros | Cons."

    This keeps answers structured and easy to digest.


  5. Use examples for clarification

    "I want my writing to sound like this: 'The sunset poured gold over the quiet hills.'
    Can you improve my paragraph to match that style?"

    Examples reduce hallucination and improve alignment with your intention.


  6. Specify what you don't want

    "Don't include historical background — just focus on current applications of AI in healthcare."

    This prevents unnecessary or off-topic output.


  7. Iterate and refine

    Ask, review, then say:
    "This is close, but could you make it more formal / concise / technical?"

    You don't have to get the perfect prompt the first time — interactive refinement is key.

Pitfalls to Avoid

Common Issue How to Fix
Too vague Add details or constraints
Unclear task Explain expected output
Overly long Break into steps

Improving Your Prompts

Prompt engineering is experimental. You may need to:

  • Rewrite from a new perspective
  • Add constraints (e.g., word count, tone)
  • Remove extra context

Test and revise your prompts for best results.

Further Tips

  • Use markdown formatting to structure prompts and outputs.
  • Chain prompts: use one output as input for the next task.
  • Ask the AI to explain its reasoning if you need clarity.

Example Transformation

Type Prompt Example
Too vague "Tell me about AI"
Better "Explain how AI is used in fraud detection in financial institutions."
Best "Act as a security engineer.
Explain how machine learning helps detect fraud in banking systems,
including common algorithms and real-world challenges."

How to Reduce Hallucinations

  • Ask for sources, assumptions, or step-by-step reasoning.
  • Example: "Can you give me a step-by-step explanation of how you arrived at this answer?"
  • Request external verification if accuracy is critical.
  • Avoid vague or open-ended questions unless you're brainstorming.
  • AI Hallucination
  • Few-shot Learning
  • Zero-shot Prompting
  • Chain-of-Thought Prompting

References