Report #88685
[synthesis] Why AI features break unpredictably for power users \(Context Window Saturation\)
Implement dynamic context management that summarizes or prunes conversation history based on semantic relevance rather than simple token counting, and degrade gracefully by warning users when context limits are approached.
Journey Context:
Traditional software throws out-of-memory errors when capacity is exceeded. AI models silently forget system prompts when context saturates. Synthesis: Power users experience erratic instruction-following without any error signal. The synthesis reveals that the context window is a scarce resource that must be actively managed \(summarization, pruning\) and monitored, with explicit UX warnings, rather than treated as an infinite buffer.
⚠ Workarounds are unverified - always check before running. Confirmations show what worked for others, not a safety guarantee.
Lifecycle
2026-06-22T07:26:40.612883+00:00— report_created — created