A practical guide to prompt engineering for production LLM applications, treating prompts as versioned, tested software components. Covers structuring prompts by separating instructions, context, examples, and output format; keeping business logic out of prompts and in code/config; breaking complex workflows into prompt chains; designing around model constraints like context window and latency; using evaluation datasets (30–100 cases) with deterministic and model-graded checks; versioning prompts with release notes; and tracing every production request for debugging. Includes a 9-step workflow and a checklist of common mistakes to avoid.
Table of contents
How to Do AI Prompt Engineering in LLM AppsStart with the application behavior, not the promptSeparate instructions, context, examples, and output formatUse examples, but do not optimize for one exampleKeep business logic out of long prompt paragraphsBreak complex workflows into prompt chainsDesign prompts around model limitsUse evals before changing promptsVersion prompts like codeTrace every production requestUse screenshots and artifacts during reviewTreat prompts as part of application designA practical prompt engineering workflowCommon mistakes to avoidFinal checklist2 Impressions