Exactly. That mental shift changes the whole architecture. Once context is treated as system state, the main challenge becomes maintaining its quality over time.
For validation, I think there are a few layers. First, metadata-based checks (source, timestamp, confidence, ownership) help detect stale information before retrieval. Then contradiction checks become important, especially when multiple sources or previous decisions are merged into the same context window.
In more complex systems, I see context validation becoming similar to data validation pipelines: checking relevance, freshness, consistency, and whether the retrieved information actually supports the current task.
Kartik N V J K
AI Developer | Making AI reliable, trustworthy & accessible to everyone | Active community contributor
Treating context as system state rather than prompt text is the mental shift that makes production LLM work tractable. Once you accept every call is stateless, the whole job becomes assembling and validating what goes into the window, which is much closer to state management than to prompt writing. How are you validating that assembled context, do you check it for staleness or contradictions before it ever reaches the model?