Memory & Long-Term Context
Give your AI app real continuity across sessions — without letting the context window quietly wreck quality.
What you'll learn
- Give your AI app real continuity across sessions — without letting the context window quietly wreck quality.
- How memory & long-term context fits into the Advanced AI Engineering track
- A hands-on project step you can put in your portfolio
The hands-on project
Design a memory system for a personal-assistant chatbot that users return to daily over months. Specify your four layers — system prompt, long-term vector memory, running conversation summary, and recent raw messages — and what belongs in each. Then write the rules: give three concrete examples of facts you'd store long-term and three you'd deliberately discard, and justify each with the 'would this matter next month?' test. Finally, describe your summarization trigger (when do you compress older turns?) and how you'd handle a stored memory that becomes stale — e.g. the user changes a stated preference.
The full brief, voice tutor walkthrough, and feedback are inside the lesson.
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