Heavy AI assistant users often face three problems: usage limits, context degradation, and losing project continuity between sessions.
I broke down why these issues happen and explored practical solutions, including context management, persistent memory, retrieval, and prompt caching.
Read the full technical guide on Causly:
https://www.causly.in/lab/why-llm-workflows-fail-heavy-developers
Top comments (3)
One thing worth remembering: a bigger context window doesn't automatically mean better results. Preserving the right information and verifying the model's output can make a real difference in long-running coding tasks.
How do you handle context loss when working on larger projects?
Usage limits and context degradation look like two problems, but they are one bill: each turn replays every message so far, so the limit runs out on the same prefix that is also crowding the answer. Cutting what gets re-sent helps both.
Exactly. The interesting part is that reducing repeated context can improve both token efficiency and context quality. But I think the key is preserving the right information, not just sending less of it. Otherwise, summarization can remove the very details needed to solve the task.