Should an internal AI assistant save summaries or make them live?
An internal built on company work data needs a clear choice: save AI-made summaries in the , or keep only the original data and summarize when someone asks a question. Saving summaries in advance can make answers cheaper and faster because the AI has less text to read later. The risk is that a wrong or outdated summary can become a trusted fact inside the system.
If the link to the original source is weak, errors can build up and become hard to trace. Keeping the raw source data and summarizing only at answer time makes easier to ground in the original material and easier to cite. Those temporary summaries can be discarded after the answer.
The tradeoff is higher latency and cost, because the model may need to reread source material for every query. For anything treated as memory, the safer direction is not to store AI-written summaries as facts, but to summarize only for the current answer with back to the source.
Key points
- Saving summaries ahead of time can lower token use and make answers faster.
- Bad or stale summaries can become trusted facts inside the .
- Summarizing at keeps answers closer to the original source.
- Query-time summaries can raise latency and cost because the model repeats work.
- For memory, storing source data is safer than storing AI-written summaries as facts.