# AI Prompt Engineering

> Design, evaluate, and refine prompts for LLMs (GPT-4, Claude, Llama, Mistral) with chain-of-thought, few-shot, RAG, and evaluation metrics.

- Skill: `quantumquirkxyz/ai-prompt-engineering` (Agent Skill)
- Install (CLI): `npx skillmds@latest add quantumquirkxyz/ai-prompt-engineering`
- Raw SKILL.md: https://api.skillmd.com/api/skills/quantumquirkxyz/ai-prompt-engineering/raw
- Safety review: pending
- Works with: Claude Code, Claude.ai, OpenAI Codex
- Category: AI & ML
- Author: quantumquirkxyz (https://skillmd.com/u/quantumquirkxyz)
- Updated: 2026-09-17
- Page: https://skillmd.com/skills/quantumquirkxyz/ai-prompt-engineering

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## Contract

- **Input:** task description, target LLM, evaluation criteria.
- **Output:** prompt library + evaluation report + recommendation.
- **Side effects:** may call LLM APIs (cost, data privacy).
- **Dependencies:** LLM API access (OpenAI, Anthropic, local model server).
- **Stop condition:** best variant selected with evaluation evidence.
- **Risk:** medium — LLM outputs can be unreliable; requires validation; data privacy concerns.
- **Boundary:** evaluates and selects prompts; does not deploy to production without additional validation.

# Prompt Engineering

Design **LLM prompts** that produce correct, consistent, and useful outputs — with evaluation, iteration, and failure-mode analysis.

## Process

### 1. Frame the task
State exactly what the LLM should do:
- Information extraction / summarisation.
- Code generation / explanation.
- Reasoning / proof / analysis.
- Creative generation.
- Classification / labeling.

**Completion criterion:** task statement precise; success criteria defined.

### 2. Design the prompt
Structure with clear sections:
- **System message:** role and constraints (e.g. "You are a careful mathematician.").
- **Context:** background, relevant data, rules.
- **Instruction:** what to do; format of output.
- **Examples:** zero-shot, few-shot (2–5 examples with input/output pairs), chain-of-thought (show reasoning steps before answer).
- **Constraints:** length, tone, forbidden content.
- **Output format:** JSON, Markdown, list, single paragraph.

**Completion criterion:** prompt structure explicit; examples selected.

### 3. Evaluate variants
Test at least 3 variants (baseline, improved, best guess):
- **Accuracy** — correct vs reference answer.
- **Consistency** — same answer across 3 runs with same prompt (temperature 0 or low).
- **Latency** — time per call.
- **Cost** — tokens used (input + output).
- **Failure modes** — what breaks (hallucination, refusal, partial answer, wrong format)?

**Completion criterion:** evaluation report with metrics and failure modes.

### 4. Iterate
Use failure modes to refine:
- Add constraints where over-generation occurs.
- Add examples where reasoning is shallow.
- Simplify instruction where confusion occurs.
- Add RAG (retrieve relevant docs) when knowledge is missing.

**Completion criterion:** at least 2 iterations; improvement documented.

### 5. Recommend
State the best variant with evidence; document when it fails; provide fallback rules.

**Completion criterion:** recommendation with justification.

## Notes

- Pair with `ai-ml-pipeline` when the prompt feeds into a model pipeline.
- Use `ai-model-evaluation` for deeper evaluation (fairness, robustness, cross-model comparison).