RAG for Developers Build, Diagnose, Ship

Design, build, evaluate, and diagnose a hybrid-search RAG pipeline on your own documents, production-ready

What you’ll learn
Design a chunking and indexing strategy and justify the vector database choice based on scale and managed vs self-hosted tradeoffs
Implement hybrid (dense + keyword) search and a reranking stage to improve retrieval precision
Apply advanced retrieval patterns based on the failure mode they address
Evaluate and diagnose a RAG pipeline with RAGAS-style metrics and continuous evaluation
Harden a RAG system against prompt injection, permission leakage, runaway embedding/generation cost, and cross-tenant data leakage
Parse real-world documents, engineer grounded prompts, and decide when long context can replace retrieval
Build a complete RAG pipeline end to end on your own document set and fix at least one injected failure

Requirements
Comfortable with Python and basic API calls
No prior RAG or vector-database experience needed

Description

This course contains the use of artificial intelligence.Retrieval-augmented generation (RAG) is how you get a large language model to answer from your own documents instead of guessing from what it memorized in training, and building one that actually works in production is a different skill from a weekend demo.

This course takes you from”what is RAG”to a working, evaluated, production-hardened retrieval-augmented generation system. You’ll build a full indexing pipeline (chunking, embeddings, vector database selection, and a hands-on vendor comparison across the real tradeoffs between vector stores), implement hybrid search and reranking, apply advanced retrieval patterns (RAG-Fusion, corrective and adaptive RAG, agentic and graph-based retrieval, and text-to-SQL for structured data), and, unlike almost every other RAG course, learn toevaluate and diagnose your system with RAGAS-style metrics, a structured failure catalog, and a continuous evaluation pipeline that catches regressions automatically, rather than treating “it looks right” as good enough.

A hands-on lesson early on builds a complete minimal RAG pipeline line by line, and a two-part capstone at the end of the core course ties everything together on your own document set, including a dedicated lesson on engineering prompts for grounded, citation-backed answers.

A optional module on production hardening (not required to finish the core course, but included) covers five things most courses skip entirely: defending a RAG system against prompt injection and permission leakage so retrieval never leaks content a user shouldn’t see, parsing real-world documents (tables, scans, images) instead of assuming clean text, choosing an embedding model and controlling embedding cost at scale through caching, knowing when a long-context model makes retrieval unnecessary versus when it doesn’t, and isolating tenant data correctly in a multi-tenant RAG deployment. This module tracks a fast-moving part of the ecosystem more closely than the rest of the course, so it’s the one most likely to see a refresh pass between rounds of edits.

This course is framework-agnostic: concepts are taught with code on screen, not tied to one version of a specific library, so what you learn keeps working after the next API changes.

What’s not in this course: fine-tuning language models, building your own embedding model from scratch, and UI/frontend chat interfaces: those are separate skills for a separate course.

Who this course is for
Junior-to-mid-level developers who want to build LLM-powered applications on their own data
Developers who have used an LLM API but never built a retrieval pipeline around it
NOT for: ML researchers wanting to train or fine-tune embedding/generation models from scratch, or frontend developers looking for chat-UI tutorials

KRAGFORDEVELOPERBUILDDIAGNOEHIPE

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RAG for Developers Build, Diagnose, Ship

RAG for Developers Build, Diagnose, Ship

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