A 5-step way to give LLM apps memory while saving tokens
do not automatically keep between requests. This workflow stores past and stable facts in a database, then adds only the useful pieces back into the prompt when needed.
The prompt is built from the system identity, relevant saved facts, and recent messages. The key idea is to stay inside a instead of sending the whole history every time.
This can make answers more consistent while reducing wasted input. It is a basic for LLM apps that need user-specific memory, not just one-off chat replies.
Key points
- usually do not remember past requests by themselves.
- and durable facts are stored in a database.
- Only relevant facts and recent messages are added to the prompt.
- A limits how much context is sent to the model.
- The pattern is useful for AI agents and apps that need ongoing user memory.