Session Management and Context Persistence
Learn to maintain conversation state and user context across multiple interactions for a seamless LLM experience.
Why LLMs Need Memory
Imagine talking to someone who forgets everything you said a moment ago. That's often how Large Language Models (LLMs) work by default!
For a truly natural and helpful experience, LLM applications need to remember past interactions. This is where session management and context persistence come in.
LLMs: Stateless by Design
When you send a prompt to an LLM API, it processes that single request independently. It doesn't inherently 'remember' any previous prompts or responses.
- Each API call is a fresh start.
- This stateless nature is efficient for simple, one-off questions.
- But it breaks down for conversations or personalized tasks.
All lessons in this course
- Distributed Caching with Redis/Memcached
- Session Management and Context Persistence
- Advanced Cache Invalidation Strategies
- Semantic Caching for LLM Responses