Prompt Engineering

Prompt engineering is the practice of crafting effective instructions and inputs for AI models to produce desired outputs. As LLMs become more capable, the ability to communicate clearly and precisely with them has become a valuable skill.

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Why Prompt Engineering Matters

Key insight: An LLM does not read your mind. It responds to the text you provide. Vague input produces vague output. Specific, well-structured input produces specific, high-quality output.

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Fundamental Techniques

Zero-Shot Prompting

Give the model a task without any examples, relying entirely on pre-trained knowledge.

Prompt: "Classify the sentiment of this review as positive, negative, or neutral: 'The food was decent but the service was terrible.'"

Output: "Negative"

Few-Shot Prompting

Provide several examples of the desired input-output pattern:

Prompt:

"Classify the sentiment:

• Review: 'Absolutely loved it!' -> Positive
• Review: 'Worst experience ever.' -> Negative
• Review: 'It was okay, nothing special.' -> Neutral
• Review: 'The ambience was lovely but the food was cold.' -> "

Output: "Mixed/Negative"

Chain-of-Thought (CoT) Prompting

Encourage step-by-step reasoning:

Without chain-of-thought

Prompt: "If a shop sells 3 apples for 2 pounds and you buy 12, how much?"

Output: "6 pounds" (potentially wrong)

With chain-of-thought

Prompt: "...Think step by step."

Output: "Step 1: 12 / 3 = 4 groups. Step 2: 4 x 2 = 8 pounds."

Tip: Phrases like "Think step by step" or "Explain your reasoning" significantly improve accuracy on analytical tasks.

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Advanced Techniques

System Prompts and Role-Based Prompting

System: "You are a senior Python developer with 15 years of experience. Write clean, documented code with type hints and error handling."

User: "Write a function that validates email addresses."

Structured Output

Instruct the model to return data in a specific format (JSON, table, bulleted list).

Self-Consistency

Generate multiple responses and take the majority answer — useful for reasoning tasks.

Prompt Chaining

Break complex tasks into sequential steps:

• Step 1: "Extract key claims from this article: [article]"
• Step 2: "Find supporting evidence for each claim: [claims]"
• Step 3: "Write a fact-check report: [evidence]"

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Best Practices