context-engineering-practices
IN premise — entries/2026/06/21/wiki-Prompt_engineering.md
Created 2026-06-21T09:50:10+00:00
Context engineering emphasizes operational practices including token budgeting, provenance tags, versioning of context artifacts, observability logging, and context regression tests
Summary
Treating the context you feed a model like a managed software artifact means you can budget how much you spend, trace where every piece came from, version it so changes are auditable, and catch regressions before they silently break behavior. In practice, this turns prompt-building from a one-off guess into a repeatable, observable engineering discipline that the system can trust and maintain over time.
Dependents
These beliefs depend on this one:
- OUT context-engineering-enables-reliable-llm-deployment — Context engineering — managing system instructions, retrieval context, token budgets, and provenance — provides a reliable methodology for deploying LLMs with consistent, controllable behavior.