How I finally got agentic RAG to work right
Blog post from Vectorize
The text discusses the challenges and innovations in developing AI agents using an approach called Agentic Retrieval Augmented Generation (RAG), which combines traditional RAG with an AI agent architecture. The author shares experiences of using large language models (LLMs) like OpenAI's GPT-4-Turbo and Claude in building these agents, highlighting issues like poor reasoning capabilities and JSON generation errors. The text emphasizes the importance of designing specific retrieval functions and optimizing data retrieval processes to improve AI performance. It also discusses the role of prompt engineering and structured responses in enhancing the accuracy and reliability of AI agents. Additionally, the author explores the potential for autonomous and multi-agent systems, where agents collaborate or operate independently to solve problems. The piece concludes with an invitation to use the Vectorize platform to overcome data engineering challenges and improve RAG system development.
| Trend | Post Mentions | Total Month Mentions | Posts | Companies | MoM |
|---|---|---|---|---|---|
| RAG | 60 | 1,936 | 254 | 78 | -19% |
| LLM | 29 | 3,889 | 441 | 129 | +7% |
| AI Agents | 16 | 576 | 82 | 45 | +82% |
| Real-time | 3 | 3,932 | 887 | 192 | +47% |
| Vector Search | 3 | 3,675 | 269 | 79 | +77% |
| Multi-agent systems | 1 | No monthly metrics for this publish month. | |||
| Voice AI | 1 | 411 | 59 | 20 | +51% |
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