Context optimization, explained for production teams.
Each guide targets one concrete problem: what it costs, why it happens, and how to fix it without cutting the context your model needs.
LLM cost optimization: a production playbook for cutting inference spend
LLM cost optimization starts with input tokens. Measure per-request spend, cut irrelevant context, then cache, route and cap output. A step-by-step plan.
RAG context compression: send fewer, better chunks to the model
RAG context compression removes redundant, off-topic and low-value chunks before the model call. How it differs from retrieval and reranking, with the math.
Agent context optimization: how to stop context bloat in multi-step agents
Agent context grows every step. Where the waste comes from (tool output, memory, stale state) and patterns that cut input tokens without hurting task success.
Context engineering: a practical guide for production AI
Context engineering is the discipline of deciding what information reaches an LLM on each call. Definition, principles, workflow, anti-patterns and metrics.
Cut your LLM costs
without cutting quality.
One layer between retrieval and inference removes the context your model never needed, in milliseconds. Fewer input tokens, lower cost, a prompt cache that keeps hitting, and quality checked on every request.