Reduce Hallucinations from LLM-Powered Agents Using Long-Term Memory

Tevin Wang discusses the development and application of AI agents in real-world scenarios, particularly focusing on the medical field's reluctance due to the risk of hallucinations in AI outputs. To mitigate this, Wang introduces "critique-based contexting," a method that improves AI decision-making using past critiques stored in vector databases like LanceDB. This approach involves embedding user input and critiques, utilizing tools such as LangChain and OpenAI's text-embedding models, to provide AI agents with a contextual foundation that reduces errors and enhances performance. By applying this method, AI agents can refine their actions based on historical data, as demonstrated through a fitness trainer example, where the agent adapts its recommendations based on critiques and past actions. The concept highlights the potential of integrating critique-based contexting into AI workflows, aiming to enhance the reliability and applicability of AI agents across various industries.