Agentic RAG: Solving Custom Vocabulary Challenges in AI

Custom vocabulary and conflicting knowledge can make or break AI performance—but Agentic RAG is here to solve these challenges.

In this video, we dive into Agentic RAG (Retrieval-Augmented Generation) and how it handles complex AI tasks like custom vocabulary, query rewriting, and conflict resolution. Whether your organization uses unique acronyms or needs precision in retrieval, Agentic RAG ensures that your AI stays accurate, clean, and confident.

Key Highlights:
🔹 Agentic RAG’s ability to adapt to custom vocabularies and corporate jargon.
🔹 The importance of clean context to avoid hallucinations and improve confidence in AI answers.
🔹 Long-term memory in LLMs to teach specific behaviors and prioritize conflicting knowledge.
🔹 How Fluid AI’s platform streamlines this process step by step.

Imagine asking an AI, “What are my points on the Chase Sapphire credit card?” Without query rewriting, the system might lose context or retrieve irrelevant data. Agentic RAG fixes this by refining the query, pulling precise information from multiple sources, and cleaning the results for a confident, accurate answer.

This video is perfect for developers, businesses, and AI enthusiasts looking to optimize AI performance and solve real-world challenges with RAG.

🔗 Want to learn more about RAG? Check out our previous videos on Vector RAG here and Graph RAG here.

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