# Prompt Engineering Patterns > Use when designing production-LLM prompts — few-shot, chain-of-thought, system prompts, templates, self-verification — distinct from prompt-optimizer and refine-prompt. - Skill: `event4u-app/prompt-engineering-patterns` (Agent Skill, multi-file: 3 files) - Install (CLI): `npx skillmds@latest add event4u-app/prompt-engineering-patterns` - Raw SKILL.md: https://api.skillmd.com/api/skills/event4u-app/prompt-engineering-patterns/raw - Safety review: pending - Works with: Claude Code, Claude.ai, OpenAI Codex - Category: AI & ML - Author: event4u-app (https://skillmd.com/u/event4u-app) - Updated: 2026-09-17 - Page: https://skillmd.com/skills/event4u-app/prompt-engineering-patterns --- # prompt-engineering-patterns Production patterns for LLM prompts: few-shot, chain-of-thought, system-prompt design, templating, self-verification. **Distinct surface** from sibling skills: - [`prompt-optimizer`](../prompt-optimizer/SKILL.md) — polishes a single end-user prompt for ChatGPT / Claude / Gemini. - [`refine-prompt`](../refine-prompt/SKILL.md) — refines a free-form work prompt into engine-ready acceptance criteria. - **This skill** — designs prompts that ship inside an application that calls an LLM at runtime. ## When to use - Designing the system prompt for a new LLM-powered feature. - Building a few-shot template with dynamic example selection. - Adding chain-of-thought reasoning to a low-accuracy prompt. - Reviewing a prompt diff in production code. - Diagnosing inconsistent LLM outputs that look like prompt drift. Do NOT use when: - Polishing a one-off prompt for a chat session — route to `prompt-optimizer`. - Turning a Jira ticket into engine input — route to `refine-prompt`. - Tuning a model's weights — this skill is prompt-only, not fine-tuning. ## Decision framework ### Step 1 — Pick the prompt level (progressive disclosure) ``` Start at Level 1; only escalate when measurement says you must. Level 1 Direct instruction "Summarize this article." Level 2 + constraints (length, format, focus) "...in 3 bullets, key findings only." Level 3 + reasoning scaffold "Read first, identify findings, then summarize." Level 4 + few-shot examples "Like these examples: ..." Level 5 + self-verification step "...then check answer against criteria; revise if fails." ``` Escalating without evidence is over-engineering. Each level adds tokens, latency, and a maintenance surface. ### Step 2 — Structure the prompt Fixed instruction hierarchy — every production prompt fills these slots in order: ``` [System context] role, expertise, constraints, safety [Task instruction] what to do, in one sentence [Examples] few-shot demonstrations (optional) [Input data] the user-supplied content [Output format] schema, length, citation rules ``` Stable slots (system, task, format) belong in cached prompt prefixes; volatile slots (examples, input) belong in the per-call portion. ### Step 3 — Pick the few-shot strategy ``` Examples are uniform and small (< 20) → embed all of them; deterministic. Examples are large or diverse → semantic-similarity retrieval per call. Edge cases dominate → diversity-sampled examples (cluster + pick one per cluster). Token budget tight → fewer, higher-quality examples beats many mediocre. Examples drift with the data → regenerate from a labeled corpus on a schedule, not hand-edited. ``` Bad examples are worse than no examples — the model imitates structure. ### Step 4 — Add chain-of-thought ONLY when measured CoT improves accuracy on multi-step reasoning, hurts on classification and lookup. Decision rule: ``` Task is multi-step / arithmetic / multi-hop → add CoT (zero-shot "let's think step by step", or few-shot CoT). Task is single-step extraction / classify → CoT adds tokens without lift; skip. You haven't measured → measure first, decide second. Self-consistency needed (high-stakes answers) → sample N reasoning paths, majority vote. ``` ### Step 5 — Build error recovery into the prompt Production prompts handle their own failure cases: - Specify the explicit "I don't know" output (don't let the model invent). - Require a confidence indicator when downstream code needs to gate. - Define the format for "missing information" so callers can branch. - For self-verification: specify the criteria, then the revision rule. ### Step 6 — Treat prompts as code - Version every prompt (file + git, not a wiki page). - Test on a frozen evaluation set before shipping changes. - Track P50 / P95 latency, token usage, accuracy, success rate per version. - A/B test prompt variants behind a flag; never edit a live prompt without a rollback path. ## Procedure: Apply to a new LLM feature 1. **Inspect** the existing prompt (if any) and the eval set; verify a success metric exists (accuracy / consistency / latency / token cost) — refuse to design without it. 2. Draft Level-1 prompt (Step 1) and measure on the eval set. 3. Escalate one level at a time (Step 1) until metric is met or budget runs out. 4. Lock the structure (Step 2), choose few-shot strategy (Step 3), decide CoT (Step 4). 5. Add error-recovery clauses (Step 5). 6. Commit prompt + eval results + chosen version (Step 6); cite this skill. ## Output format 1. Prompt-spec table: slot · content · stable-vs-volatile · cached-vs-per-call. 2. Eval results table: prompt-version · metric · delta-vs-previous. 3. Failure-mode list: trigger · prompt clause that handles it. ## Gotcha - Few-shot examples leak the model's style — examples that include hedging produce hedging. - "Let's think step by step" works zero-shot on capable models, fails on smaller models without exemplar reasoning traces. - Self-consistency (N samples + vote) multiplies cost by N — only on high-stakes paths. - Cached prompt prefixes only cache when byte-identical — a single reformat busts the cache. - Prompts that drift across model versions silently regress accuracy when the provider rolls a model update; pin model version OR re-run eval per release. ## Do NOT - Do NOT escalate to Level 4 / 5 before measuring at lower levels. - Do NOT mix few-shot examples from different tasks; the model averages them. - Do NOT add CoT to single-step classification — it hurts. - Do NOT hand-edit production prompts without versioning + eval. - Do NOT echo secrets or PII into the prompt — they end up in provider logs. ## Auto-trigger keywords - prompt engineering - few-shot learning - chain-of-thought - system prompt design - prompt template - LLM prompt versioning - prompt evaluation ## Provenance - Adopted from: an external reference (internal provenance, redacted) — restructured into a decision-framework shape; source-specific vendor Python snippets dropped. - Cross-linked: [`prompt-optimizer`](../prompt-optimizer/SKILL.md), [`refine-prompt`](../refine-prompt/SKILL.md), [`mcp-builder`](../mcp-builder/SKILL.md), [`async-python-patterns`](../async-python-patterns/SKILL.md). - Provenance registry: `agents/settings/contexts/skills-provenance.yml` (entry: `prompt-engineering-patterns`). - Iron-Law floor: `verify-before-complete`, `skill-quality`, `non-destructive-by-default`.