What is context engineering? A practical guide for LLM applications

Context engineering is the request-time practice of assembling everything an LLM receives beyond its core model weights, including system instructions, examples, conversation history, retrieved documents, memory, tool outputs, user data, and application metadata. Unlike prompt engineering, which focuses on instructions, it manages the selection, ranking, freshness, ordering, formatting, compression, deduplication, conflict resolution, and token allocation of changing inputs within a limited context window. Effective workflows gather candidate information, filter and rerank it, resolve duplicates and contradictions, compress it to fit a defined budget, log the final payload, and evaluate both the context and resulting answer. Key measures include context precision and recall, groundedness, utilization, task success, token share, and freshness, while common failures include missing or ignored evidence, silent truncation, stale memory, contradictory sources, context bloat, permission leakage, and history crowding out relevant information. The text presents Braintrust as a platform for tracing assembled contexts, creating regression datasets from production failures, defining deterministic and model-based scorers, running ablation experiments, enforcing evaluation gates in CI, and monitoring context quality after deployment.

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