prompt-engineering

name	prompt-engineering
description	System prompt design, chain-of-thought patterns, structured output, and prompt versioning Use when this capability is needed.
metadata	{"author":"dtsong"}

Prompt Engineering

Purpose

Design, evaluate, and version system prompts for LLM-powered features, including instruction structure, chain-of-thought patterns, output format constraints, and few-shot example selection.

Inputs

• Feature requirements (what the LLM should do)
• Input data format and examples
• Desired output format and constraints
• Quality requirements (accuracy, consistency, tone)
• Cost and latency constraints (model selection guidance)

Process

Step 1: Define the Task Precisely

Before writing a prompt, articulate:

• Input: What exactly does the model receive? (user message, context, data)
• Output: What exactly should it produce? (classification, generation, extraction, transformation)
• Constraints: What must it never do? (hallucinate facts, reveal system prompt, produce PII)
• Edge cases: What happens with empty input, adversarial input, ambiguous input?

Step 2: Structure the System Prompt

Use a layered structure:

1. Identity and role: Who is the model in this context?
2. Task description: What it's being asked to do (one paragraph, precise)
3. Constraints and rules: Hard rules it must follow (numbered list)
4. Output format: Exact structure of the expected output (with template)
5. Few-shot examples: 2-3 input/output pairs showing ideal behavior
6. Edge case handling: What to do when uncertain, off-topic, or missing data

Step 3: Design Chain-of-Thought (if applicable)

For complex reasoning tasks:

• Explicit CoT instruction: "Think step by step before answering"
• Structured CoT: Define the reasoning steps (e.g., "1. Identify the entities, 2. Determine relationships, 3. Synthesize answer")
• Hidden CoT: Instruct model to reason in a <thinking> block, then provide the answer separately
• When to skip CoT: Classification tasks, simple extraction, and format conversion rarely benefit

Step 4: Design Output Format

Specify the exact output structure:

• JSON output: Provide a schema with required fields, types, and constraints
• Markdown output: Provide a template with section headers
• Structured extraction: Define the fields and valid values explicitly
• Validation: How will the output be parsed? Design the format for reliable parsing.

Step 5: Select and Craft Few-Shot Examples

Choose examples strategically:

• Cover the range: Include examples representing different input types
• Include edge cases: Show desired behavior for tricky inputs
• Show the format: Examples should match the exact output format
• Keep it minimal: 2-3 examples are usually enough; more can confuse
• Order matters: Put the most representative example last (recency bias)

Step 6: Design Versioning Strategy

Plan for prompt evolution:

• Version identifier: Semantic versioning (v1.0, v1.1, v2.0)
• Storage: Version-controlled alongside code (not in a database, not hardcoded)
• A/B testing: How to run two prompt versions simultaneously
• Rollback: How to revert to a previous version quickly
• Changelog: What changed and why, linked to eval results

Output Format

# Prompt Design: [Feature Name]

## Task Definition
**Input:** [Description + example]
**Output:** [Description + format]
**Constraints:** [Hard rules]

## System Prompt (v1.0)

[Full system prompt text]

## Few-Shot Examples
### Example 1
**Input:** [Example input]
**Expected output:** [Example output]

### Example 2
**Input:** [Edge case input]
**Expected output:** [Edge case output]

## Chain-of-Thought Strategy
[Whether CoT is used, what the reasoning structure looks like]

## Output Schema
```json
{
  "field1": "string (required) — description",
  "field2": "number (optional) — description"
}

Prompt Versioning

Version	Date	Change	Eval Score
v1.0	[Date]	Initial	[Score]

Model Recommendation

Recommended: [Model] at [temperature] Rationale: [Why this model for this task] Cost estimate: [$/1K requests]

## Quality Checks

- [ ] System prompt has a clear role, task, constraints, format, and examples
- [ ] Output format is designed for reliable programmatic parsing
- [ ] Few-shot examples cover normal cases and at least one edge case
- [ ] Constraints address prompt injection, hallucination, and off-topic input
- [ ] Prompt is stored in version control with a changelog
- [ ] Model and temperature are justified for the task requirements

## Evolution Notes
<!-- Observations appended after each use -->

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