Retrieval Augmented Generation Basics with JS

Overview

Master RAG fundamentals in JavaScript by exploring basic concepts and retrieval techniques, constructing and querying vector databases, using semantic embeddings, and building a complete end-to-end RAG pipeline in JS.

Syllabus

Course 1: Introduction to RAG with JavaScript
Course 2: Text Representation Techniques for RAG Systems
Course 3: Scaling up RAG with Vector Databases using JavaScript
Course 4: Beyond Basic RAG using JavaScript: Improving the Pipeline

Courses

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1 hour
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Learn what Retrieval-Augmented Generation (RAG) is, why combining retrieval with generation can reduce hallucinations, and how a basic RAG workflow contrasts with naive prompting. This course is mostly informational, setting the stage for more hands-on work in later courses.
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2 hours
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Learn practical text‐representation methods for RAG systems: why representation matters, Bag-of-Words vs. semantic embeddings, visualizing embeddings with t-SNE, and comparing their performance in document retrieval and semantic search.
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2 hours
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Build and query a vector database for RAG: preprocess documents, store chunk embeddings in ChromaDB, retrieve relevant chunks with metadata filters and weighting, craft multi-chunk prompts, manage collection updates, and scale ingestion with batch strategies.
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2 hours
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Enhance your RAG pipeline with advanced retrieval: combine BM25 and embeddings, implement iterative retrieval with query refinement and context building, constrain LLM outputs to sourced context, and optionally summarize chunks into a concise prompt.