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Many developers report that assistant quality drops during long sessions. The model starts missing instructions, rewriting unrelated sections, or suggesting inconsistent edits.
That usually does not mean the model suddenly got worse. It often means context quality degraded over time.
Why Long Sessions Drift
As threads grow, your prompt competes with earlier edits, failed attempts, and unrelated files. The more stale context in play, the easier it is for the assistant to prioritize noise over intent.
Practical Fixes
- Use short, task-scoped chats and start a fresh thread after each completed unit of work.
- Reference only the files needed for the change; avoid broad codebase scope for narrow edits.
- Ask for plan-first output on risky tasks, then execute in small reviewed diffs.
- Treat each response as a draft and review before applying broad edits.
Use Spec-First Prompts
Structured specs reduce ambiguity and cut retry loops. BrainGrid helps you define scope and constraints before prompting.