Google OKF + RAG: The Ultimate AI Agent Architecture

Описание: Should you build your AI agent's knowledge base with RAG (Vector Databases), Google's new OKF (Open Knowledge Format), or both? The answer isn't as simple as choosing one. RAG gives your AI unlimited reach across millions of documents but can quietly introduce hallucinations through chunking and probabilistic retrieval. OKF delivers deterministic, version-controlled answers you can trust, but it doesn't scale to messy, unstructured knowledge. The architecture that actually wins is a Hybrid AI Stack.

In this video, Kais breaks down exactly how modern AI retrieval works under the hood. We explain Retrieval-Augmented Generation step by step, from document chunking and embeddings to vector databases, approximate nearest-neighbor search, cosine similarity, and prompt injection. Then we dive into Google's Open Knowledge Format, showing how plain Markdown files, Git version control, and deterministic retrieval create a trustworthy knowledge layer for AI agents.

Finally, we combine both approaches into a production-ready architecture using an intelligent router that sends high-stakes, canonical questions to OKF while routing open-ended, exploratory queries to RAG. You'll learn why an 80/20 split between curated knowledge and semantic search dramatically reduces hallucinations while keeping unlimited search capabilities.

⏱️ TIMESTAMPS:
00:00 - 00:50: Why AI answers confidently... and wrong
00:50 - 01:45: How RAG actually works
01:45 - 02:25: Meet Google's Open Knowledge Format (OKF)
02:25 - 03:05: Why neither RAG nor OKF is enough
03:05 - 05:45: RAG explained step by step
05:45 - 08:15: How OKF works under the hood
08:15 - 10:45: Build a hybrid router
10:45 - 13:20: Real-world architecture and examples
13:20 - End: When to use RAG, OKF, or both

#rag #googleokf #aiagents #vectordatabase #llm #retrievalaugmentedgeneration #systemdesign #machinelearning #langchain #llamaindex

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