Practical guide to managing LLM context windows for AI engineers building production systems. Covers creating token budgets with hard caps per context section, structuring prompts with a context plan instead of dumping all available data, and eight concrete techniques: reserving output tokens first, ranking context by task value, summarizing conversation history, trimming retrieved chunks, controlling tool-result size, keeping tool schemas route-specific, ordering instructions strategically, and using few-shot examples sparingly. Includes a before/after example showing how to reduce a bloated support agent context, a common-mistakes checklist, and guidance on evaluating context changes like code changes.
Table of contents
How to Manage an LLM Context WindowWhat the context window actually containsStart with a token budgetUse a context plan, not a context dumpAnnotated prompt and message payloadBefore and after: reducing bloated contextPractical techniques for managing contextCommon mistakes to avoidEvaluate context changes like code changesA simple context management checklistUse the smallest reliable context199 Impressions