How Does AI Assistant Memory Work?

Why models forget by default

Large language models have a limited context window — the amount of text they can consider at once. Anything outside that window is invisible to them. Without a memory system, each new conversation starts blank, which is why a plain chatbot keeps asking you to re-explain your situation.

Memory solves this by saving the important parts of your interactions somewhere persistent, then feeding the right pieces back into the model when they're relevant.

How persistent memory works, step by step

• Capture: the assistant identifies durable facts worth keeping (your goals, preferences, project details, decisions).
• Store: those facts are saved outside the model — often in a database and/or a vector store for semantic search.
• Retrieve: on each new request, the system pulls the most relevant memories based on what you're doing right now.
• Inject: the retrieved context is added to the prompt so the model answers with full awareness of your history.
• Update: as things change, memories are revised or expired so the assistant stays current.

Project-scoped vs. global memory

Simple memory is one rolling profile about you. More useful is project-scoped memory, where context is attached to the specific project it belongs to. That way, details about your product launch don't bleed into your personal finances, and the assistant brings the right context to the right work.

Why it matters

• Less repetition — you stop re-explaining your situation
• More personalized, consistent help over time
• The assistant can act with full context, not guesswork
• Work compounds: the more you use it, the more useful it gets

AI42's approach

AI42 organizes memory around your projects, so the assistant remembers the context that belongs to each piece of work and brings it forward when relevant. The result is an assistant that feels like it actually knows what you're working on — not a stranger every session. You can try it with $1 of AI Power and no credit card.