Posted by shloked 2 days ago
In either case, I’ve turned off memory features in any LLM product I use. Memory features are more corrosive and damaging than useful. With a bit of effort, you can simply maintain a personal library of prompt contexts that you can just manually grab and paste in when needed. This ensures you’re in control and maintains accuracy without context rot or falling back on the extreme distortions that things like ChatGPT memory introduce.
So many times my solution when stuck with an LLM is to wipe the context and start fresh. I would be afraid the hallucinations, dead-ends, and rabbit holes would be stored in memory and not easy to dislodge.
Is this an actual problem? Does the usefulness of the memory feature outweigh this risk?
It will be very interesting to see which approach is deemed to "win out" in the future
The explicit memory is what you see in the memory section of the UI and is pretty much injected directly into the system prompt.
The global embeddings memory is accessed via runtime vector search.
Sadly I wish I could disable the embeddings memory and keep the explicit. The lossy nature of embeddings make it hallucinate a bit too much for my liking and GPT-5 seems to have just made it worse.