LLM stack video: embeddings to MCP

A new video breaks down the full technology stack engineers use to build real-world apps with large language models, from turning text into searchable data to connecting AI agents with external tools. (youtube.com) Embeddings convert words, images or code into numerical vectors—think of them as coordinates on a map where similar meanings cluster close together. Engineers feed content into models like OpenAI's text-embedding-3-large to generate these vectors for quick similarity searches. (platform.openai.com) Vector databases such as Pinecone or Weaviate store billions of these embeddings and retrieve the most relevant ones in milliseconds using techniques like approximate nearest neighbors. This solves the problem of sifting through massive datasets without exact keyword matches. (pinecone.io) Retrieval-Augmented Generation, or RAG, pulls those top embeddings and feeds them into an LLM like GPT-4o as context, grounding its responses in specific facts to cut hallucinations by up to 70% in benchmarks. Companies like Anthropic use RAG in production apps to answer queries over private documents. (anthropic.com) AI agents go further by breaking complex tasks into steps—such as researching a topic, summarizing findings, then emailing results—using frameworks like LangChain or LlamaIndex to orchestrate multiple LLM calls. OpenAI's Swarm library, released in 2024, lets agents hand off subtasks dynamically. (github.com/openai/swarm) The video highlights MCP-style interfaces, inspired by Anthropic's Model Context Protocol, which standardize how agents connect to tools like APIs or databases via simple JSON schemas. This turns fragmented tool calls into a plug-and-play system, reducing integration bugs. (anthropic.com) Engineers stack these layers end-to-end: embeddings feed retrieval, RAG grounds generation, agents plan actions, and MCP links tools—forming one architecture that powers apps from customer support bots to code assistants. The 18-minute video by ML engineer Harrison Chase uses live demos to show it running on a laptop. (youtube.com) This coherent framing arrives as RAG adoption hit 40% of enterprise LLM projects in Q1 2026, per Gartner, amid hype fatigue over standalone demos. (gartner.com) Chase draws from his work on LangChain, which has powered over 1 million apps since 2022, to emphasize production reliability over lab tricks. "These aren't buzzwords—they're the stack," he says in the video. (langchain.com) Watch it to build your first agentic app; the repo with code drops next week. (youtube.com)