Prompt Patterns That Consistently Work with LLMs

Learn reliable prompt patterns to get predictable, high-quality outputs from large language models — practical templates, testing tips, and a quick implementation checklist.

Prompt engineering is about patterns, not tricks. Use repeatable structures to steer LLMs toward useful, concise, and controllable outputs across tasks and models.

• TL;DR: use explicit role, constraints, examples, and output format.
• Prefer few-shot + clear instructions for new tasks; zero-shot for simple queries.
• Iterate with targeted tests and evaluation metrics to refine behavior.

Try this quick interactive idea: open a text editor, paste a template from “Ready-to-use templates”, and swap the {{INPUT}} tokens to experiment with tone and length.

Quick answer — 1-paragraph summary

Use a clear role statement, desired format, and constraints: “You are a . Given , produce

in with .” Add 1–3 examples for complex tasks. This reliably focuses the model and reduces hallucinations while providing predictable structure for parsing or downstream workflows.

Why these patterns still work

LLMs are statistical text generators that respond best to strong contextual signals. Role statements set an implicit prior, format/spec constraints narrow the model’s token distribution, and examples anchor behavior. Together, these elements reduce ambiguity and align outputs with user intent.

Concretely, patterns work because they:

• Provide a clear distribution of expected tokens (format)
• Reduce open-endedness (constraints and length limits)
• Offer demonstrations that the model can imitate (few-shot)

Core prompt patterns to use

Below are concise, reusable patterns. Replace angle-bracket tokens with your content.

• Role + Task + Format

  You are a {role}. Given: {input}. Output: {result type} in {format}. Constraints: {constraints}.

• Instruction + Example (few-shot)

  Instruction: {task}. Example 1: Input: {i1} → Output: {o1}. Example 2: Input: {i2} → Output: {o2}. Now: Input: {input} →

• Step-by-step decomposition

  Step 1: {analysis}. Step 2: {plan}. Step 3: {final output}.

• Constraint-first (for safety/format)

  Do not include: {forbidden}. Must include: {required}. Return only: {machine-friendly format}.

Match patterns to tasks

Choose patterns based on task complexity and risk.

Adapt patterns for new models

Different models vary in temperature, context window, and instruction-following fidelity. Start by tuning a few parameters and prompts:

• Lower temperature for factual or deterministic outputs; raise for creativity.
• Shorten examples if context window is limited; prefer single-shot with strong constraints.
• Run calibration prompts: same prompt across models to compare consistency and adjust instructions accordingly.

When switching models, validate on representative inputs and measure differences in hallucination rate, length variance, and response time.

Ready-to-use templates (copy/paste)

Replace tokens like {{INPUT}}, {{LENGTH}}, and {{TONE}}.

• Concise summary

  You are an expert summarizer. Summarize the following text: {{INPUT}}. Output: one paragraph, max {{LENGTH}} words, neutral tone. Do not add new facts.

• Customer email rewrite

  You are a professional customer success writer. Rewrite this email for a {{TONE}} tone and keep key facts: {{INPUT}}. Output only the rewritten email.

• Bug-to-ticket converter

  You are a product triage assistant. Given: {{INPUT}}. Produce a ticket with fields: Title, Severity (low/med/high), Steps to reproduce, Expected result, Actual result, Attachments summary.

• SEO meta generator

  You are an SEO specialist. From this page content: {{INPUT}}, generate: 1) Title (<=60 chars), 2) Meta description (<=155 chars), 3) 5 focus keywords. Output as JSON only.

Test, evaluate, and refine prompts

Systematic testing is essential. Use small, repeatable experiments and track metrics.

• Define evaluation criteria: accuracy, brevity, format correctness, hallucination rate.
• Use a test set of 50–200 representative inputs for statistical reliability.
• Automate checks: regex or schema validation for structured outputs, and human reviews for nuance.
• Iterate: change one variable at a time (e.g., add example, tighten constraint) and measure impact.

Common pitfalls and how to avoid them

• Vague instructions — Remedy: add role, output format, and explicit constraints.
• Overlong context — Remedy: prune irrelevant info, summarize or provide pointers to external content.
• Model drifting (inconsistent style) — Remedy: include style examples and post-process normalization.
• Hallucinations — Remedy: require source citations or mark "I don't know" when unsure; validate with retrieval or grounding.
• Too-strict templates that block creativity — Remedy: allow a short "creative" section after constrained output.

Implementation checklist

• Define role, task, and required output format for each prompt.
• Create 2–3 concise examples for complex tasks.
• Add explicit constraints and forbidden content lists.
• Set evaluation criteria and build a test set.
• Run A/B tests across model parameters and iterate.
• Integrate validation (regex/schema) before downstream use.

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