JavaScript-Powered AI: Crafting Intelligent Systems with LLMs and Vector Databases
summary
Builds a JavaScript RAG pipeline that actually reduces support load by grounding LLM responses in org knowledge and returning citations, using OpenAI APIs, LangChain, and Azure AI Search as the vector database with React/Express/Node front and back ends. Reports ~35% of tickets as simple step‑by‑step fixes and estimates ~50% lower wait time. Shows embeddings, chunking to control tokens, semantic retrieval from the vector DB, streaming responses and a deployed prototype that integrates updates back into the index.
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chapters (10)
- 0:00Introduction
- 4:02LLMs as tools
- 9:23Support ticket case study
- 12:12AI grounding with RAG
- 17:09Solution technologies
- 18:56RAG key considerations
- 21:04Embeddings and vector DBs
- 26:37Token management
- 31:32Prompt templates
- 33:16Resources and closing
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