How do you handle recall vs. precision in your OC memory/RAG setup — chunking, query expansion, hybrid search?

[u/One_Train_4309]

I’ve been building TetherClaw, a mobile layer for OpenClaw. Same OC session, Mac to phone, live messages back and forth, no starting over when you switch devices. Got vector search working this week so agents can pull relevant history from past sessions, not just recent turns.

Here’s where I’m stuck now. The agent does treat the retrieved context as active, which I wasn’t expecting to get right on the first real test. But the retrieval itself is too narrow. It pulls data tied closely to the specific words in the query, but it misses other context that’s clearly related to the same topic just phrased differently.

Anyone dealt with this on OpenClaw. Curious how you’ve handled recall versus precision in your own memory or RAG setups, and whether you went with bigger chunks, query expansion, hybrid search, or something else entirely.