After helping hundreds of teams integrate MemorySync into their production applications, we have seen exactly what works and what fails. If you want your AI memory to be fast, accurate, and cheap, follow these battle-tested patterns.
1. Store Facts, Not Conversations
The absolute most common mistake developers make is piping raw, 20-turn chat transcripts directly into their memory database. This creates massive, noisy vectors that are impossible to search accurately.
Do not do this: *"User: I like pizza. AI: Great! User: Especially with pepperoni. AI: Yum! User: But no mushrooms."*
Do this: Use a background LLM to summarize that conversation into a single, atomic fact: *"User prefers pepperoni pizza and dislikes mushrooms."* Storing crisp facts ensures perfect recall.
2. Scope Memories as Narrowly as Possible
Never put all your users' memories into one massive global pool. You must scope memories hierarchically. At minimum, use: `Tenant ID` -> `User ID` -> `Project ID`.
When your AI goes to retrieve memory, it should only search within that specific User's Project bucket. This drastically reduces the search space, making retrieval lightning fast and significantly reducing the chance of hallucinating another user's data.
3. Handle Contradictions Gracefully
Users change their minds. A user might say "Write my code in Python" in January, but then say "Actually, we use TypeScript now" in March.
If your system blindly stores both, the AI will get confused. You must design your system to handle contradictions. The easiest pattern is to append a `timestamp` to every memory, and explicitly instruct your LLM in the prompt: *"If you see conflicting memories, prioritize the one with the most recent timestamp."*
4. Don't Inject Everything
Just because an AI has 1,000 memories about a user does not mean you should inject all 1,000 into every prompt. This burns through tokens and distracts the LLM (the "Lost in the Middle" phenomenon).
Use semantic search to pull only the Top 5 most relevant memories for the specific question being asked. If the user asks about coding, pull the coding memories. Leave the pizza preferences in the database.
5. Give Users Control
Always build a UI in your application that allows users to see what the AI remembers about them. Let them manually delete or edit incorrect memories. Not only is this legally required for privacy compliance, but it also builds massive trust with your user base.