Reduce Hallucinations from LLM-Powered Agents Using Long-Term Memory
Blog post from LanceDB
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.
| Trend | Post Mentions | Total Month Mentions | Posts | Companies | MoM |
|---|---|---|---|---|---|
| Vector Search | 22 | 1,161 | 174 | 75 | -27% |
| LLM | 9 | 1,935 | 244 | 98 | -1% |
| AI Agents | 6 | 71 | 24 | 11 | -25% |
| Multi-agent systems | 1 | 2 | 2 | 2 | -90% |
| RAG | 1 | 144 | 33 | 19 | -9% |
| Serverless | 1 | 919 | 144 | 74 | +58% |
Use this post, company, and trend context to find content marketing opportunities, perform competitive analysis, or address product feature gaps via the Plushcap MCP server or the Plushcap API.