ai-context-engineer

Token budgeting for LLM prompting allocates available tokens across system blocks, tool definitions, retrieved context, and conversation history to minimize waste. You configure a base context policy to distribute tokens effectively before processing requests.

History compression for AI agents rolls up long conversations while preserving goals and decisions. This context engineering technique summarizes prior interactions to maintain agent continuity without exceeding token limits.

Caching in LLM retrieval pipelines reuses stable prefixes and preloads retrieved documents to reduce latency. By storing repetitive prompt segments, you avoid recomputing context and lower overall response times.

Debugging context assembly failures involves instrumenting requests to reproduce how prompts are built, identifying lost instructions, token overflow, or distraction. You trace the assembly pipeline to fix missing system blocks or retrieved context.

Building structured context blocks for AI agents is supported through context pipelines that assemble prompts and retrieval paths. You define a context policy with system blocks and tool definitions to feed agents relevant data.