RAG & AI Agents

Building Your First RAG Pipeline

AI Agents: From Concept to Implementation

RAG Fundamentals: Build Your First Retrieval-Augmented Generation System

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.

Vector Databases Compared: Pinecone vs Weaviate vs pgvector for Production RAG

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.

Vector Databases: Pinecone, Weaviate, and pgvector Compared - Complete Implementation Guide

Learn to implement semantic search with the three leading vector database platforms. Build working examples, compare performance, and understand when to use each technology in production systems.

Building a RAG Pipeline with Your Own Documents

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.

Chunking Strategies: How to Split Documents for Better Retrieval

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.

Building Multi-Step AI Agents with Planning and Memory

Learn to create sophisticated AI agents that can plan complex workflows, adapt strategies based on results, and learn from experience through persistent memory systems.

Evaluating RAG Systems: Precision, Recall, and Faithfulness

Learn to build comprehensive evaluation pipelines for RAG systems using retrieval metrics, end-to-end quality assessment, and faithfulness measurement to prevent hallucinations.

Hybrid Search: Combining Keyword and Semantic Search for Better Results

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.

Production RAG: Caching, Monitoring, and Continuous Improvement

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.

Enterprise RAG: Security, Permissions, and Multi-Tenant Architecture

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.

Embeddings Explained: How Text Becomes Vectors for Semantic Search

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.

Reranking Retrieved Results: Implementing Cross-Encoders to Improve RAG Accuracy

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.

Agentic RAG: Building Self-Correcting Retrieval Pipelines That Query, Reflect, and Retry

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.

Prompt Engineering for RAG: How to Structure System Prompts That Ground LLM Responses in Retrieved Context

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.

Tool-Augmented AI Agents: Giving Your Agent Web Search, Code Execution, and API Access

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.

Graph RAG: Building Knowledge Graph-Enhanced Retrieval Pipelines for Complex Multi-Hop Queries

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.

Query Routing in RAG: How to Direct Questions to the Right Data Source or Retrieval Strategy

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.

Contextual Compression in RAG: Filtering and Compressing Retrieved Chunks Before Passing to the LLM

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.

Corrective RAG with Hallucination Detection: Automatically Validating and Replacing Unreliable Retrieved Documents at Runtime*

Understanding LLM Context Windows: How Token Limits Shape RAG Design Decisions

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.

Metadata Filtering in RAG: Using Structured Attributes to Narrow Retrieval Before Vector Search

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.

Multi-Agent Orchestration: Building RAG Systems Where Specialized Agents Collaborate Across Data Sources

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.

Retrieval-Augmented Generation vs. Fine-Tuning: When to Use Each Approach and Why

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.

Implementing Late Chunking with Long-Context Embeddings to Preserve Document Structure in RAG

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.

Adaptive Retrieval: Dynamically Adjusting Chunk Count and Search Strategy Based on Query Complexity at Runtime

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.

Query Expansion in RAG: Hypothetical Document Embeddings and Multi-Query Retrieval

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.

Parent Document Retrieval: Index Small Chunks, Return Rich Context for Better RAG*

Structured Output Contracts for AI Agents: Enforcing JSON Schemas, Retry Logic, and Validation Pipelines in Production

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%.

Document Ingestion Pipelines: Loading, Cleaning, and Preprocessing Text for RAG

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.

Tool Memory vs. Retrieval Memory in AI Agents: Implementing Short-Term, Long-Term, and Episodic Memory Architectures

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.