AI & Machine Learning

Prompt Engineering

The practice of designing and optimizing inputs (prompts) to AI language models to elicit desired outputs, improving accuracy, relevance, and usefulness of responses.

Also known as:Prompt DesignPrompt Optimization

What is Prompt Engineering?

Prompt engineering is the practice of crafting effective inputs to large language models to achieve desired outputs. It involves understanding how models interpret instructions and using techniques to guide their responses toward specific goals.

Key Techniques

Zero-Shot Prompting Direct instruction without examples.

Classify this review as positive or negative: "Great product!"

Few-Shot Prompting Providing examples to guide the model.

Review: "Loved it!" -> Positive
Review: "Terrible!" -> Negative
Review: "Amazing quality" -> ?

Chain-of-Thought Encouraging step-by-step reasoning.

Let's solve this step by step...

System Prompts Setting context and behavior rules.

You are a helpful assistant that...

Best Practices

  • Be specific and clear
  • Provide context and constraints
  • Use structured formats
  • Iterate and refine
  • Test edge cases
  • Consider safety and ethics

Advanced Techniques

  • ReAct (Reasoning + Acting)
  • Tree of Thoughts
  • Self-consistency
  • Constitutional AI prompts
  • Role-playing prompts

Common Pitfalls

  • Ambiguous instructions
  • Missing constraints
  • Overly complex prompts
  • Ignoring model limitations
  • Not handling edge cases
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Privileged Access Management (PAM)

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Prompt Injection