RAG: answers from your documents, not from the internet.
Retrieval-Augmented Generation means the model pulls the relevant fragments from your documents before answering and answers from them, with the source shown. That removes the main objection to LLMs in work tasks: you can't tell where an answer came from or whether to trust it.
Preparing the corpus
Parsing documents, chunking, removing duplicates and outdated versions. The boring part — and quality depends on it more than on the choice of model.
Retrieval that finds the right thing
Vector search alone often misses on terminology and part numbers. A hybrid with keyword search plus reranking is usually required.
An answer with its source
The user sees which document and which passage the answer came from. Without that, people don't start trusting the system no matter how much you improve it.
Quality evaluation
A set of real questions with correct answers, showing whether a change made things better or worse. Otherwise improvements are made blind.
What is RAG in plain words?
Before answering, the model searches your documents for relevant pieces and answers from them. Like an employee who checks the manual before replying instead of answering from memory.
Why RAG rather than fine-tuning?
Documents change. RAG updates with them: put in a new version of the manual and the system answers from it. Fine-tuning can't do that and costs more.
Will our documents leave our perimeter?
Only if you allow it. For sensitive corpora we use self-hosted models or a setup where a minimum leaves the perimeter.
How many documents make this worthwhile?
It's not about the count but about how much time people spend searching. If an answer is found in half a minute by ordinary search, RAG isn't needed.
What RAG actually is and when a business needs it
RAG means the model answers from your documents rather than from the internet, with a visible source under every claim. What it is made of, where quality is lost in chunking and updates, how to measure that it works and when you do not need it.
Chunking documents for RAG: why chunks matter more than the model
A bad RAG answer almost always comes from chunking rather than from the model. How to split policies, contracts, scanned PDFs and support tickets, what breaks on a corpus of tens of thousands of long documents, and how to tell that chunking is the culprit.
How to update a RAG knowledge base without stopping the system
A demo lives on a static corpus, production on a moving one. Incremental updates instead of full reindexing, swapping the index without a restart, deleting documents, and which metrics reveal retrieval degradation before users report it.
Checklist: are your documents ready for RAG
PDF, 2 pages. No sign-up, no email — just the file.