Why a RAG System Retrieves the Wrong Documents
When a RAG system returns the wrong document, the language model is rarely the cause. The failure usually sits in chunking, retrieval, or ranking. This guide explains…
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5 published articlesRetrieval-Augmented Generation: A Practical Guide to RAG Systems
Retrieval-Augmented Generation lets a language model answer from evidence in your own documents instead of relying only on its training data. This guide explains the four stages…
How to Check Whether an AI Citation Actually Supports a Claim
An AI citation supports a claim only when the cited source, read in context, actually states that claim. This guide explains how to resolve citations, compare claims…
What Is an AI Context Window? Tokens, Limits, and Practical Workarounds
A context window is the amount of input an AI system can process at one time, measured in tokens and including the response it is generating. This…
RAG vs Fine-Tuning: How to Choose an Approach for Your Project
Start with retrieval augmented generation (RAG) when your project must answer questions from documents that change and cite sources. Choose fine-tuning when the model needs to match…