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RAG Architecture Explained: Retrieval-Augmented Generation
RAG gives a language model the right pages to read before it answers. This guide walks through the four stages with a pipeline that runs on a laptop, using open models and public documents, and shows three outcomes: a correct cited answer, a retrieval miss the model admits to, and a question outside the documents.
Fine-Tuning vs RAG: Which Should You Choose?
Fine-tuning and RAG are often presented as rivals. They change different things: retrieval gives a model facts to read, and fine-tuning changes how it behaves. This guide shows the difference with a test on a local model, summarises the research and gives an order to try things in.
Retrieval Evaluation: Measuring RAG Quality
You cannot improve a RAG system you have not measured. This guide computes hit rate, MRR and nDCG on 40 real questions, then shows two things most tutorials skip: how wide the uncertainty is on a small test set, and why a retriever that looks better may not be better.
Chunking Strategies for RAG: Getting Retrieval Right
How you cut documents into chunks decides what a RAG system can find. This guide tests five chunking strategies on the same 40 questions and finds no single winner: splitting rescued facts buried in long sections, lost others that needed their surroundings, and section titles mattered more than size or overlap.
Building a Document Q&A Chatbot with RAG
Answering one question about a document is the easy part. A chatbot also gets follow-ups such as which one, and why. This guide builds a document Q&A chatbot over a 48-page PDF with page citations, runs one conversation two ways, and shows how rewriting follow-ups turned a wrong answer into a right one.
Knowledge Graphs and RAG: Combining Structure with Search
Vector search finds passages that sound like the question. Some questions need facts that are counted, filtered or connected instead. This guide builds a knowledge graph from the headers of 744 Python Enhancement Proposals, answers four structured questions exactly, and shows vector search failing the same filter.