A checklist of prompting patterns for practical work
Start by treating prompting as a design activity rather than a single sentence to craft, and write down the desired outcome before you begin testing prompts. Define the user, the model role, the exact deliverable and any acceptance criteria you will use to judge success, and note these in a short prompt brief to keep iterations focused and reproducible.
Choose a pattern that matches the task and complexity instead of guessing what will work best, and keep a short list of go-to patterns for common categories of work. For routine transformations favour templates and instruction prompts, for creative synthesis try role-based or persona prompts, for reasoning and multi-step tasks use chain-of-thought or decomposition patterns, and for data extraction rely on few-shot examples to show the exact expected structure.
Provide clear context and exemplars inside the prompt so the model has the information and format cues it needs to perform reliably, and include one or two well-chosen examples that reflect edge cases. Where possible include labelled examples that match the output format you expect, and consider adding a short note about where the examples came from so you can test for domain drift later.
Specify constraints and the output contract explicitly to reduce back-and-forth, including limits on length, required fields, tone, and any forbidden behaviours or content. If the output will be processed downstream by parsers or systems, describe the exact structure or use a machine-readable format such as JSON, CSV or markdown tables to avoid ambiguity and simplify automated checks.
Iterate with small, measurable changes and keep a changelog of prompt edits so you can trace regressions and improvements, and adopt rapid A/B style tests for variants that differ by a single modification. Use automated sampling where practical to assess variance in responses across the same prompt, and record both correctness and confidence indicators to assist prioritisation of prompt fixes.
Validate outputs with unit-style checks and a small human review loop before scaling, and create a short checklist of acceptance tests that can be run for each new prompt or version. Include sanity checks for hallucination, factual consistency and formatting, and add regression tests that run whenever you update templates, examples or role instructions to catch unintended side effects early.
Plan for operational integration by deciding how prompts will be stored, versioned and accessed in your workflow, and ensure you have a governance note that describes who can change prompts and what testing is required before deployment. Keep a single source of truth for prompt templates and link to relevant resources for team training, such as the AI automation tag on the blog for curated posts and examples about production practices, and review prompts periodically to optimise for cost and performance. For more builds and experiments, visit my main RC projects page.
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