Large Language Models Have Poor Memory? Techniques for Making Your AI Have "Memory"
You might have noticed that ChatGPT is very good at chatting, but it forgets what you've said after some time. Why can't AI models 'remember' the content you've spoken about? This article dives into the theoretical aspects to explain the memory limitations of large language models and introduces several implementation methods that enable an AI assistant capable of learning, accumulating, and carrying on with context.
Why do Large Language Models have a "poor" memory?
LLM (Large Language Model) is inherently 'stateless', it can only output answers based on the current provided prompt. If you don't tell it about past content, it truly doesn't know. Even though OpenAI has introduced a 'memory' feature, it merely stores context in external records.
Three Ways to Build AI's Memory
1. Temporarily Store Chat Context
Every user input is recorded and continuously fed into the model. The drawback is that context length has limits, causing early conversations to gradually fade away.
2. External Memory Database (like vector databases)
Important dialogues, settings, personal preferences are converted into Embedding vectors for storage. When needed next time, the model retrieves and supplements information based on semantic meaning.
3. User-specific Data Cards (Profile + History)
Alike 'the AI assistant remembers what you like', each user maintains a set of personal preferences and operational history. This uses RAG technology to proactively fill in information.
Application Scenarios
- Customer service system remembers historical questions and solution records
- An AI assistant remembers your personal preferences, work style, and to-do items
- Sales assistants provide tailored suggestions based on customer attributes
How NT Tech Assists?
NT Tech excels in integrating vector databases, proprietary models, and frontend user systems to create enterprise-level AI assistants with 'memory' capabilities:
- Supports management of multi-user, multi-turn context
- Records and queries individual preferences and historical interactions
- Combines internal knowledge base with personal records to dynamically generate responses
If you want to build an AI assistant that 'gets to know you the more you chat', memory design is key!
Contact NT Tech for an AI assistant capable of remembering you
