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RAG
4 articles
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How AI Agents Remember: A Layman’s Guide to Embeddings and Vector Databases
A practical introduction to how AI agents use embeddings and vector databases for semantic memory, retrieval, and grounded long-running work. Read more → -
What Is AI Grounding? Preventing Hallucinations in Commercial AI Applications
AI grounding anchors model outputs to verified sources instead of relying only on training data. Learn how grounding works with RAG, agents, verification, and practical commercial systems. Read more → -
OpenAI Is Moving Away from Fine-Tuning. Why Most People Shouldn't Train Their Own Model
Fine-tuning is rarely the right first step. This article breaks down its hidden data, engineering, maintenance, and opportunity costs, then compares prompting, RAG, and ready-made agents for practical AI work. Read more → -
RAG vs AI Agents: Why a Knowledge Base Alone Isn't Enough for Real Work
RAG improves answers with external knowledge, but real work needs agents that plan, use tools, collaborate, and close task loops. Learn the differences and when each approach fits. Read more →