Memory (AI)
Systems that allow AI to retain and recall information across conversations. Can be short-term (within session) or long-term (across sessions).
In-Depth Explanation
Memory in AI systems refers to mechanisms that allow models to retain and recall information beyond a single interaction. This is crucial for creating coherent, personalised AI experiences.
Types of AI memory:
- Context memory: Information held within a single request's context window
- Conversation memory: History of the current conversation session
- Long-term memory: Information persisted across sessions
- Episodic memory: Specific past interactions and events
- Semantic memory: General knowledge and facts
- Procedural memory: Learned processes and workflows
Implementation approaches:
- Context stuffing: Include relevant history in the prompt
- Summarisation: Compress conversation history
- Vector storage: Store and retrieve relevant past context
- Database integration: Persist structured information
- Knowledge graphs: Store relationships and entities
Challenges:
- Context window limits
- Determining what to remember
- Memory management at scale
- Privacy implications
- Hallucination about remembered content
Business Context
Memory enables personalised AI experiences where the system remembers customer preferences, past issues, and conversation history.
How Clever Ops Uses This
We implement sophisticated memory systems for Australian businesses, enabling AI assistants that truly know their customers and remember important context across interactions.
Example Use Case
"A support bot that remembers your previous issues, preferences, and account details across sessions, providing personalised service."