rag
Progress from zero to frontier with a guided depth ladder.
RAG Evaluation and Guardrails — How to Keep Answers Useful and Grounded
A practical guide to measuring RAG quality and implementing guardrails that reduce hallucinations in production.
RAG Freshness and Staleness: The Part Builders Underestimate
Why retrieval quality is not enough in RAG systems: freshness, index staleness, update pipelines, and trust in changing knowledge bases.
RAG for Builders: The Mental Model You Actually Need
A clear technical model for Retrieval-Augmented Generation: when to use it, where it fails, and what to measure.
RAG in Production: Architecture Decisions That Actually Matter
Building a RAG system that works in production is harder than the demos suggest. A deep dive into the architecture decisions, failure modes, and engineering tradeoffs that determine whether your RAG actually works.
Agentic RAG: When Retrieval Needs Reasoning
Standard RAG retrieves and generates. Agentic RAG reasons about what to retrieve, evaluates results, and iterates — handling complex queries that single-shot retrieval can't answer.
RAG Chunking Strategies: Why Your Split Matters More Than Your Model
Chunking is the most underrated decision in RAG system design. The wrong strategy degrades retrieval quality regardless of how good your embedding model is. Here's how to do it right.
RAG for Code: Building Documentation-Aware Developer Tools
RAG over code and documentation is different from RAG over prose. Here's how to build retrieval systems that understand codebases and deliver contextually relevant results to developers.
RAG Document Parsing: Getting Clean Text from Messy Documents
A practical guide to parsing documents for RAG systems — handling PDFs, slides, spreadsheets, and web pages, with strategies for preserving structure, tables, and images.
Evaluating RAG Systems: How to Know If Your Pipeline Is Actually Working
Building a RAG pipeline is straightforward. Knowing if it's actually working is hard. Here's a systematic approach to evaluating retrieval quality, generation quality, and end-to-end RAG performance.
Evaluation Metrics for RAG Systems
How to measure whether your RAG system actually works — retrieval metrics, generation metrics, and end-to-end evaluation frameworks.
RAG for Code: Building Documentation-Aware Developer Tools
How to build RAG systems that understand codebases and documentation — from chunking strategies for code to embedding models that handle technical content to retrieval patterns for developer tools.
Hybrid Search for RAG: Combining Dense and Sparse Retrieval
Pure semantic search often underperforms in production RAG systems. Hybrid search — combining dense embeddings with sparse retrieval — is the more reliable approach.
Metadata Filtering in RAG: The Most Underrated Retrieval Technique
Semantic search alone isn't enough for production RAG. Metadata filtering — combining vector similarity with structured filters — dramatically improves retrieval precision.
Multi-Index RAG: Searching Across Different Knowledge Bases
Real-world RAG systems rarely have one monolithic index. This guide covers architectures for searching across multiple knowledge bases, merging results, and routing queries to the right index.
Parent Document Retrieval: Solving RAG's Context Window Problem
Small chunks retrieve better but provide less context. Large chunks provide context but retrieve worse. Parent document retrieval solves this tradeoff — search on small chunks, return the full document.
Query Rewriting for RAG
Bad retrieval often starts with a weak query. Here's how query rewriting improves RAG systems, which strategies work, and how to avoid turning a simple question into a worse one.
Query Understanding for RAG: What Happens Before Retrieval
The quality of RAG output depends more on understanding the query than on the retrieval algorithm. Query classification, expansion, decomposition, and routing determine whether the right documents ever reach the LLM.
RAG for Real-Time Data: Streaming and Live Sources
How to build RAG systems that work with real-time data—streaming ingestion, live index updates, event-driven architectures, freshness guarantees, and the engineering challenges of keeping retrieval current.
RAG Reranking: Getting the Right Chunks into the Context Window
First-pass retrieval is fast but imprecise. Reranking adds a second stage that dramatically improves which chunks actually reach the LLM. This is the technical guide to reranking strategies in production RAG.
RAG Security: Access Control, Data Isolation, and Prompt Injection Defense
How to secure RAG systems — from document-level access control and multi-tenant data isolation to defending against prompt injection through retrieved documents.