intermediate~3h
Conversational Memory
Techniques for persisting context across multiple conversation turns in LLM chat applications: full buffer, windowed buffer, summary memory, and entity memory — and how to choose between them given context window constraints and conversation length.
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Subtopics
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Exercises
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Projects
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Quiz Qs
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Flashcards
▶📚 Prerequisites(2)
🎓 Learning objectives
- •Explain why LLMs are stateless and what that means for multi-turn conversations
- •Implement conversation buffer memory and identify when it exceeds context limits
- •Apply windowed buffer memory with the correct window size for a given use case
- •Implement summary memory that compresses old conversation history while preserving key facts
- •Choose the right memory strategy given context window size, conversation length, and what needs to be remembered
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📂 Subtopics
Related concepts
context windowcontext engineeringRAGtoken budgetstateless APIconversation buffersummary compressionentity extractionsession stateLangChain memory
Next to learn
context-engineering →rag-workflow →agent-patterns →