28 articles
Building a RAG system that works in production is harder than it looks. Avoid common mistakes like bad chunking and missing metadata by understanding that RAG is a dynamic system, not just a static database.
A 3,000+ word technical and visionary guide to Context Engineering and Data Molecules. Learn why RAG is no longer enough and how structured data packaging prevents AI hallucinations in production.
Data privacy is the #1 hurdle for enterprise AI. Learn how to architect a production-grade Role-Based RAG system that ensures users only see what they are authorized to access, from ingestion to real-time retrieval.
How can we make AI reliable enough for a bank or a hospital? In our final lesson of Module 7, we explore the industry best practices for silencing the 'LIAR' in the machine.
How are businesses actually using those big lists of numbers? In our final lesson of Module 3, we look at semantic search, RECOMMENDATIONS, and the basics of RAG.
Does a 2-million-token context window make RAG obsolete? A benchmark study on 'Lost in the Middle' phenomena versus optimized vector search for agentic workflows.
Moving beyond simple vector search. How Agentic RAG uses multi-step reasoning, query decomposition, and corrective feedback to answer complex questions.
Chat with your data. Learn how to build a Retrieval-Augmented Generation (RAG) system that allows an AI to answer questions using your company's private PDF or Google Doc files.
High-Fidelity Search. How to use AgentCore to make your RAG systems more reliable by adding pre-search and post-search nodes.
Hands-on: Design an AgentCore workflow that uses a critic node to verify RAG output.
Foundations of RAG. Why Knowledge Bases are the secret to building AI that 'knows' your private business data.
The RAG APIs. Understanding the difference between raw retrieval and the fully managed 'Answer' API.
Proving the Answer. How to extract citations and references to show users exactly where the AI found its information.
Hands-on: Build a Python script that answers questions about your S3 documents and prints the citations.
From Chatbot to RAG. Three hands-on projects to prove your mastery of Generative AI fundamentals.
Connecting AI to Reality. How to ground AI responses in your own private data to prevent hallucinations.
The Final Challenge. Build a production-ready, agentic RAG system that analyzes companies and returns structured research reports via a REST API.
Fighting Hallucinations. Understanding the architectural pattern of grounding AI responses in factual, retrieved context.
The Search Object. How LangChain standardizes vector store lookups into a 'Retriever' that can be used in any chain.
Piping Facts. Putting it all together into a single LCEL chain that retrieves context and generates an answer.
The Art of Grounding. How to write the perfect system prompt to ensure your AI stays factual and cites its sources.
Hands-on: Finalize your first production-ready RAG system over your own local documents.
Hands-on: Build a simple RAG agent that retrieves context from a local Vector DB before answering.
The two speeds of learning. Understanding conversation buffers vs vector databases.
Fixing the memory problem. How Retrieval-Augmented Generation gives local AI a 'library' to consult.
How to slice your data. Techniques for breaking large documents into AI-sized pieces without losing context.
Connecting the dots. How a user's question travels through the vector store and back to the LLM.
Hands-on: The complete RAG project. Index a folder of text files and build a bot that can answer questions about them.