Your AI assistant answered the same question perfectly yesterday.
Today, same question, it starts from scratch. No memory of the previous conversation.
You explain your preferences again. Your context again. Your history again.
Every conversation feels like talking to someone with amnesia.
The AI is not broken. It was never designed to remember. Memory is something you have to build.
INTERMEDIATE - Builds on vector databases and context management. Enables persistent AI behavior.
AI models have no memory by default. Each request starts fresh. Memory architectures are the patterns you implement to give AI the illusion of continuity: what happened before, what matters now, and what to bring back when relevant.
Think of it as building a filing system for your AI. Working memory holds the current task. Short-term memory keeps the recent conversation. Long-term memory stores important facts that persist across sessions. The architecture determines what goes where and when to retrieve it.
The choice is not whether to add memory, but which type. Working memory for the task at hand. Episodic memory for past interactions. Semantic memory for learned facts. Most systems need all three working together.
Every system that needs continuity requires memory layers. Without them, you repeat yourself, lose context, and start over constantly. The pattern is universal: recent stuff stays accessible, important stuff gets stored, and everything else can be retrieved when needed.
Separate what is immediately relevant (working memory), what happened recently (short-term), and what matters long-term (persistent). Route information to the right layer based on importance and recency.
Click "Send Next Message" to step through the conversation. Watch how different types of information get routed to different memory layers.
Click below to start the conversation
Empty - awaiting task
Empty - no recent context
Empty - no learned facts yet
What the AI is thinking about right now
The current conversation, the current task, the current context. Lives in the prompt itself. Limited by the context window. Resets between sessions. Fast but temporary.
Recent interactions worth keeping briefly
The last few conversations, recent preferences, recent corrections. Stored in a database with timestamps. Retrieved when the same user returns. Summarized or trimmed over time.
Persistent facts that matter indefinitely
User preferences, learned facts, important context that should never be forgotten. Stored in vector databases for semantic retrieval. Retrieved when relevant to the current query.
A user returns to your support assistant after a week. Without memory, the AI asks for their name again, their preferences again, their issue history again. With memory architecture, the AI greets them by name, applies their communication preferences, and recalls their open issues.
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You crammed the entire user history into every prompt. Context window exploded. Costs tripled. The AI got confused by irrelevant old information and gave worse answers.
Instead: Keep working memory minimal. Move history to short-term storage. Retrieve only what is relevant to the current query.
"User prefers dark mode" and "User asked about pricing once" got the same storage priority. Now your retrieval returns trivia instead of preferences. The AI forgot what actually matters.
Instead: Score memories by importance. Preferences and corrections are high-value. One-off questions are low-value. Retrieve high-value first.
Memory kept growing forever. After 6 months, retrieval returned outdated information. User changed their preferences but the AI kept recalling the old ones.
Instead: Implement decay or versioning. Newer memories override older ones. Set TTL for ephemeral data. Version preferences so updates replace old values.
You've learned how to give AI persistence across sessions. The natural next step is understanding how to compress and manage what goes into the context window.