Learn how RAG combines document retrieval with AI generation to create accurate, source-backed responses. Build a complete RAG system from scratch with practical Python examples.
Master the critical decision of choosing between Pinecone, Weaviate, and pgvector for your RAG system. Learn implementation patterns, performance benchmarking, and when each database excels in production.
Learn to build a production-ready RAG system that ingests your real-world documents, creates semantic search capabilities, and generates accurate answers with proper source attribution.
Master four proven document chunking techniques that dramatically improve AI retrieval accuracy. Learn when to use fixed-size, semantic, sentence-based, and structure-aware approaches with hands-on examples.
Learn to create sophisticated AI agents that can plan complex workflows, adapt strategies based on results, and learn from experience through persistent memory systems.
Learn to build comprehensive evaluation pipelines for RAG systems using retrieval metrics, end-to-end quality assessment, and faithfulness measurement to prevent hallucinations.
Learn to build search systems that combine the precision of keyword matching with the intelligence of semantic understanding. Master the balance between exact matches and contextual relevance.
Transform your RAG prototype into a production-grade system with multi-layer caching, comprehensive monitoring, and automated improvement loops that learn from real usage patterns.
Master the complex security challenges of enterprise RAG systems. Learn to implement multi-tenant isolation, fine-grained permissions, and performance-optimized security controls that scale across organizational boundaries.
Keyword search breaks down the moment users describe problems in their own words. This lesson explains exactly how embedding models transform text into numbers that capture meaning — and walks you through building a working semantic search system from scratch in Python.
Your RAG pipeline's retrieval quality determines everything downstream — and bi-encoder vector search has a fundamental limitation that reranking solves. Learn how to implement a production-grade cross-encoder reranking stage that measurably improves answer accuracy without rebuilding your pipeline from scratch.
Standard RAG pipelines fail silently when retrieval goes wrong — and in production, it goes wrong constantly. This expert-level lesson builds a full agentic RAG system with relevance grading, query rewriting, and hallucination detection using LangGraph and structured LLM outputs. By the end, you'll have a pipeline that knows when it's wrong and fixes itself.
Most RAG pipelines fail not because the retrieval is wrong, but because the model wasn't told how to use what it retrieved. This lesson teaches you exactly how to write system prompts that keep LLM responses tightly grounded in your documents — with real examples and test strategies.
Language models alone can't access today's data, run precise calculations, or call external systems. Learn how to build production-ready tool-augmented agents with web search, sandboxed code execution, and REST API integration — including error handling, memory, and cost management.
Standard RAG fails when answers require tracing relationships across multiple pieces of information. Graph RAG combines vector search with knowledge graph traversal to handle exactly these multi-hop queries—and this lesson shows you how to build the full pipeline from scratch, including ingestion, entity extraction, graph construction, and hybrid retrieval.
Most RAG systems have more than one data source — and blindly searching all of them for every question is a recipe for slow, noisy, expensive answers. Learn how to build an intelligent query router that sends each question exactly where it needs to go.
Raw vector search retrieves the right documents but passes too much noise to your LLM. This lesson teaches you how to build compression pipelines that strip retrieved chunks down to only what's actually relevant to the query — reducing token costs and improving answer quality at the same time.
Context windows are the most underestimated constraint in RAG system design. This lesson teaches you exactly how tokens work, how your context budget gets divided, and how to make smarter decisions about chunking, retrieval, and model selection as a result.
Pure vector similarity search isn't enough for production RAG systems — temporal drift, cross-domain contamination, and tenant isolation require structured filtering before or alongside embedding search. This lesson teaches you to design metadata schemas, implement pre- and post-filtering in Pinecone and Chroma, and dynamically extract filter conditions from natural language queries using an LLM.
Single-agent RAG systems break when your data lives across SQL databases, document archives, and live APIs — because one retriever can't serve all those sources well. This lesson teaches you to build multi-agent RAG architectures with specialized agents, a working orchestrator, and production-grade failure handling.
Two of the most powerful techniques for customizing AI systems — but they solve completely different problems. Learn how RAG and fine-tuning actually work under the hood, when each one is the right choice, and how to build a decision framework you can apply to your own projects.
Standard RAG pipelines lose critical document context the moment they split text into chunks. Late chunking fixes this by running the full document through a long-context embedding model first, so every chunk vector inherits the semantic weight of the entire document — without any extra LLM calls. This lesson builds the complete implementation from scratch.
Most RAG systems use the same fixed top_k for every query — a one-size-fits-all approach that degrades quality for both simple and complex questions. This expert lesson shows you how to build a runtime adaptive retrieval orchestrator that classifies incoming queries along multiple complexity dimensions and dynamically routes each one to the right strategy with a calibrated chunk count, without sacrificing latency.
Standard RAG fails when user questions don't share vocabulary with the documents they're searching. This hands-on lesson teaches you two production-grade techniques — Multi-Query Retrieval and Hypothetical Document Embeddings — that dramatically improve retrieval recall in real RAG systems.
Language models don't honor data contracts — you have to enforce them. This deep-dive lesson teaches you how to build production-grade validation pipelines for AI agent outputs, combining JSON Schema grammar constraints, Pydantic business logic validators, and intelligent retry loops that inject error context back into the model to achieve self-correction rates above 90%.
Before your RAG system can answer questions intelligently, it needs clean, well-structured text to work with. This hands-on lesson walks you through building a complete document ingestion pipeline in Python — from loading PDFs and Word files to cleaning noise, chunking text, and enriching chunks with metadata.
Most AI agents forget everything the moment a session ends — and that's an engineering choice, not a limitation. This lesson teaches you to build a three-layer memory system combining in-context buffers, vector-based long-term retrieval, and structured episodic storage. By the end, your agent will remember users across sessions, retrieve relevant past context automatically, and know when to look things up explicitly.
Most RAG latency problems aren't in the LLM — they're in the retrieval layer. This expert-level lesson covers HNSW and IVF index internals, quantization, multi-tier caching, and hardware-level tuning to get end-to-end retrieval consistently under 100ms at production scale.
Your RAG system was accurate on day one — but what happens when documents change? This lesson teaches you how to build a production-grade vector store from scratch, implement hash-based change detection, and choose between three practical update strategies to keep your index in sync as your document corpus evolves.
Most RAG pipelines silently ignore tables, charts, and diagrams — the parts of documents that often contain the most critical data. This lesson builds a complete multimodal RAG pipeline that extracts, indexes, and retrieves text, tables, and images together, then feeds them to a vision-capable LLM for accurate, grounded answers.
Most RAG pipelines fail not because retrieval is bad or the LLM is wrong — they fail because nothing validates what goes in, filters what comes out, or enforces organizational policy in between. This lesson teaches you to build production-grade guardrails layer by layer, from prompt injection detection to grounding-based hallucination checks, with a policy engine that lets your compliance team update rules without a deployment.
Before you build a RAG pipeline, you need to understand what "similar" actually means in vector space. This lesson breaks down cosine similarity, dot product, and Euclidean distance from first principles — with working Python code and real retrieval scenarios — so you can make the right choice every time.
Hybrid RAG pipelines combine keyword and semantic search — but merging their results without breaking both is harder than it sounds. This lesson teaches you Reciprocal Rank Fusion from the formula up through a complete, production-ready Python implementation, so you can build retrievers that outperform either approach alone.
Single-turn RAG breaks the moment users start asking follow-up questions. This deep-dive lesson teaches you how to build genuine conversational memory into your RAG pipeline — with query reformulation, rolling history compression, session persistence, and retrieval deduplication that actually works in production.
RAG systems fail in predictable ways — and most developers spend hours guessing when they should be diagnosing. This lesson breaks down the five core retrieval failure modes, shows you how to distinguish them from generation failures, and gives you concrete diagnostic tools to find and fix the root cause every time.
Generic embedding models don't understand your domain's vocabulary, synonyms, or conceptual relationships — and that gap silently kills retrieval accuracy. This lesson teaches you how to fine-tune a sentence transformer on your own data, from synthetic training pair generation to quantitative evaluation and production deployment.
Most RAG systems are semantically blind to time — they'll happily surface a two-year-old policy over the current one if the embedding scores align. This expert-level lesson teaches you how to build temporal metadata schemas, freshness-weighted scoring, version conflict resolution, and query-intent classifiers that make your RAG system genuinely time-aware.
Your embedding model determines what "similar" means in your RAG system — and it's the single biggest lever over retrieval quality. This lesson compares OpenAI, Sentence Transformers, and Cohere side by side with working code, a decision framework, and a hands-on evaluation harness you can run on your own data.
HyDE bridges the gap between how users ask questions and how documents store answers — by asking an LLM to hallucinate a plausible answer and using that embedding to search your corpus. This hands-on lesson walks you through building HyDE for both dense vector search and BM25 from scratch, including multi-hypothetical averaging and hybrid fusion.
Pure dense retrieval fails on exact product codes, legal citations, and rare terminology. This expert lesson teaches you how BM25 and SPLADE learned sparse encoders work, how to implement both alongside dense vectors, and how to fuse them into a production hybrid RAG pipeline that handles all query types reliably.
Most RAG systems treat knowledge as a bag of document chunks. Knowledge graphs let AI reason *across* facts — following chains of relationships the way a human expert does. This lesson teaches you the fundamentals from scratch, with working Python code and clear connections to modern RAG and agent architectures.
Tool failures are inevitable in production AI agents — the question is whether your system handles them gracefully or catastrophically. This lesson builds a complete fallback chain framework from error classification through LLM-aware partial result handling, with real code you can deploy today.
Speculative RAG parallelizes draft generation and retrieval to dramatically cut response latency — then verifies and upgrades the draft against retrieved evidence. This lesson teaches you to build the full pipeline, tune the acceptance threshold, and integrate it into production with proper observability.
Most RAG systems fail not because of bad embeddings, but because they treat every document as a featureless blob of text. This lesson teaches you how to design and attach structured metadata at ingestion time, enabling filtered and faceted retrieval that returns contextually appropriate results — not just semantically similar ones.
Your RAG system has the right documents but still returns irrelevant chunks — because hyper-specific queries embed into vectors that miss the general-language paragraphs that actually answer them. This lesson shows you how to implement stepback prompting to abstract queries up one level of generality before retrieval, with production-ready code and evaluation strategies.
Long-running LLM agents that crash mid-execution lose all their progress — and restarting from scratch wastes time, money, and API calls. This expert lesson teaches you how to design and implement a complete agent checkpointing system with tool call caching, rollback to arbitrary prior states, and transparent resume — so your agents survive network failures, rate limits, and any other production hazard.
Most RAG evaluations test the average case and miss the hard ones. Needle-in-a-haystack testing systematically exposes where your retrieval breaks down — by document length, information position, and collection scale. Learn how to build, run, and interpret NIAH tests to harden your pipeline before your users find the gaps.
Standard RAG systems fail at cross-document synthesis because they're fundamentally flat. RAPTOR fixes this by recursively clustering and summarizing your corpus into a multi-level tree — making thematic queries as retrieval-friendly as specific ones. This lesson walks you through building a complete, production-grade RAPTOR pipeline from scratch.
Most multi-agent RAG systems fail not because agents are bad at retrieval or reasoning, but because they can't communicate reliably. This lesson teaches you how to build production-grade inter-agent protocols with typed message schemas, concurrent-safe shared state, and coordination patterns that handle real-world failures.
Large language models are powerful but suffer from frozen knowledge and no access to your private data. This lesson explains exactly how Retrieval-Augmented Generation solves both problems — covering the full pipeline from document ingestion to vector search to grounded LLM generation, with working code and real design tradeoffs.