Build a RAG Knowledge System

About This Lab

Build a Retrieval-Augmented Generation (RAG) system: ingest documents, embed them, store in a vector database, and connect to an LLM for grounded responses. This is how enterprise AI, legal research tools, and financial intelligence platforms work.

Lab Modules

1. 1
   RAG architecture and embedding theorylesson

   Chunking, embeddings, cosine similarity, retrieval strategies.

   25 min
2. 2
   Ingest + embed your document corpusbuild

   PDF loader, text splitter, OpenAI ada-002 embeddings.

   45 min
3. 3
   Build the retrieval + generation pipelinebuild

   Similarity search, rerank, prompt construction, source attribution.

   40 min
4. 4
   Deploy + evaluate accuracydeploy

   RAGAS metrics, precision, recall, faithfulness scoring.

   30 min

What You'll Build

• Ingest and chunk a large document corpus (PDFs, web pages, Markdown)
• Embed documents and store in a vector database
• Build a grounded Q&A system with source citations
• Evaluate RAG accuracy with test queries

Tools & Stack