Prompt Engineering

Why This Matters

The difference between a useless LLM response and a brilliant one is often just the prompt. Prompt engineering is the art and science of crafting inputs that reliably produce the outputs you want.

The Intuition

Think of prompt engineering like giving instructions to a very capable but very literal intern. They'll do exactly what you ask — so you need to be precise about what you want, provide examples of good output, and specify constraints.

Core Techniques

1. Role Setting

Tell the model who it is:

You are an expert Python developer specializing in async programming.

2. Few-Shot Examples

Show, don't just tell:

Convert these to SQL:
- "all users from New York" → SELECT * FROM users WHERE city = 'New York'
- "orders over $100" → SELECT * FROM orders WHERE total > 100
- "active subscriptions" →

3. Chain of Thought

Ask the model to reason step by step:

Think through this step by step before giving your final answer.

4. Output Format Specification

Be explicit about structure:

Respond in JSON with keys: summary, confidence (0-1), sources (array of URLs)

5. Constraints

Set boundaries:

Use only information from the provided context. If unsure, say "I don't know."

Common Anti-Patterns

See Also

• RAG — Retrieving context to include in prompts
• Multi-Agent Orchestration — When one prompt isn't enough
• Context Composition In Practice — How Amprealize automates prompting