context-packaging

To prevent context drift during LLM handoff, package source-grounded, freshness-dated, evidence-backed context with explicit non-goals and completion criteria. This ensures the AI agent acts on bounded tasks without inventing contracts or relying on stale assumptions.

The best way to package minimal context is to produce a smallest sufficient, task-scoped package tailored to your specific change request. This includes exact contract references, citeable facts, and named completion criteria rather than dumping whole repositories.

Separate facts from assumptions in AI coding context by enforcing strict categorization into FACT, INFERENCE, ASSUMPTION, and OPEN QUESTION. This source-grounded evidence approach uses citeable references like path, line ranges, and ADRs.

You need to revalidate context packages when explicit drift triggers or freshness markers indicate source changes. This drift control mechanism ensures the AI agent operates with valid, up-to-date information across cross-session coordination or context refreshes.

Yes, context packaging works for cross-session coordination and minimizing context-window overflow risk. By providing smallest sufficient context packages with contract inclusion, it prevents overload and maintains task scope across sessions.

Include API contracts in AI coding task handoff by providing exact references or excerpts rather than paraphrases. This contract-accurate approach defines objective completion criteria and explicit non-goals the agent must not touch.