Prompt Engineering Tips — Complete Guide (2)

Prompt engineering is the fastest, cheapest lever you have for improving LLM output. No fine-tuning, no extra compute — just text. But "add more detail" isn't a strategy. Here are the techniques that consistently produce measurable improvements.

Anatomy of a Well-Structured Prompt

Every high-performing prompt has four layers:

1. System Context: Who the model is and what constraints apply.
2. Task Definition: Exactly what you want, in specific terms.
3. Input Data: The content the model should operate on.
4. Output Format: The structure, length, and format of the response.

Chain-of-Thought (CoT) Prompting

For reasoning tasks, adding "Let's think step by step" increases accuracy measurably. But you can do better by providing an explicit reasoning scaffold:

Prompt Template
You are a senior software architect. Analyze the following system design problem.

Problem: {problem}

Use this reasoning structure:
1. CONSTRAINTS: List all hard requirements.
2. RISKS: What could go wrong with naive approaches?
3. OPTIONS: Name 3 possible architectures.
4. RECOMMENDATION: Pick one and justify it.
5. IMPLEMENTATION: Give a 5-step roadmap.

Begin your analysis:

Few-Shot Learning

Including 2–3 input/output examples in your prompt steers the model toward the exact pattern you need. Especially powerful for formatting tasks:

Few-Shot Example
Extract the company name and sentiment from each review.

Review: "Stripe's API docs are fantastic, saved us days."
Output: {"company": "Stripe", "sentiment": "positive"}

Review: "AWS support took 3 weeks to respond, totally unacceptable."
Output: {"company": "AWS", "sentiment": "negative"}

Review: "{new_review}"
Output:

Advanced Techniques