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 (4)
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.
@knihal12 Agreed, and the hard part is deciding what counts as the right information. Dropping the failed attempts usually costs less than dropping the constraints the agent learned the hard way.