Module 01: Introduction
Module 02: Naive RAG
Module 03: Data Engineering
Module 04: Query Intelligence
Module 05: Advanced Retrieval
Module 06: Agentic Architectures
Module 07: Evaluation
Bulletproof RAG: Production Curriculum
A comprehensive sequence on building production-grade, grounded AI systems using Vercel AI SDK v6, LangChain, and unpdf.
Welcome to the Bulletproof RAG curriculum. This course is designed for senior developers moving beyond the "toy RAG" phase into building reliable, self-correcting, and multimodal retrieval systems. We standardize our stack on the Vercel AI SDK v6, providing a unified interface for embeddings, neural search, and agentic reasoning.
Curriculum Structure
The curriculum is divided into seven distinct modules, tracing the lifecycle of a production RAG pipeline:
- 01 Introduction: Structural rationale and stack configuration.
- 02 Naive RAG: Implementing the baseline and identifying its production failure points.
- 03 Data Engineering: PDF parsing, recursive segmentation, and contextual metadata injection.
- 04 Query Intelligence: Automated rewriting, multi-query expansion, and HyDE search.
- 05 Advanced Retrieval: Neural reranking, hybrid fusion, and contextual compression.
- 06 Agentic Architectures: Self-corrective loops (CRAG), tool-calling, and multimodal vision patterns.
- 07 Evaluation: Observability, logging foundations, and pipeline monitoring.
Technical Principles
Every module in this course adheres to three core engineering principles:
- Accuracy First: We prioritize grounding over conversational fluff.
- Observability: Every technique is accompanied by structured, timestamped trace logs.
- Modularity: We use a decoupled stack (unpdf + AI SDK) to ensure future-proof portability.
Select a module on the left to begin implementation.