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 — polishes a single end-user prompt for ChatGPT / Claude / Gemini.
• refine-prompt — 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, refine-prompt, mcp-builder, async-python-patterns.
• Provenance registry: agents/settings/contexts/skills-provenance.yml (entry: prompt-engineering-patterns).
• Iron-Law floor: verify-before-complete, skill-quality, non-destructive-by-default.