RAG (Retrieval-Augmented Generation) is a technique that connects a language model to your internal documentation: given a question, it first retrieves the relevant fragments from your documents and then generates the answer from them, citing its sources. It is the most reliable way for your team to query company knowledge in plain language, with answers that are precise and easy to verify.
It is a pattern that César García Cabeza, an AI consultant in Andorra, applies routinely when designing documentation-query systems for companies.
"Eighty percent of a RAG system's quality is decided before you ever touch the model: in how you split the documents and in cleaning out duplicated or stale sources. The model is almost never the bottleneck."
— César García Cabeza, AI consultant in Andorra
How does RAG work under the hood?
The name gives it away: it combines retrieval and generation. The flow looks like this:
- Indexing: your documents are split into fragments and turned into numerical representations (embeddings) stored in a vector database.
- Retrieval: when someone asks a question, the system finds the fragments most relevant to it.
- Generation: the model writes the answer using only those fragments as context, and includes citations back to the source.
The big advantage over a plain LLM is that the answer rests on your documents, not on whatever the model happens to remember from its training.
Why do citations matter so much?
Citations are what turn RAG into a tool people actually trust. When an answer links to the source document and section, the reader can verify it in seconds. That:
- Lowers the risk of acting on wrong information.
- Builds trust in the system, which drives adoption.
- Makes it easier to spot stale or contradictory documentation.
A well-built RAG system, when it finds nothing relevant, says so rather than inventing an answer.
What mistakes should you avoid?
These are the failures we see most often when teams roll out RAG:
- Poor chunking: splitting documents with no real criteria breaks the context and degrades the answers.
- Dirty documentation: if the sources are outdated or duplicated, RAG amplifies the problem. The output quality never beats the input quality.
- Ignoring permissions: each user should only see what they are entitled to; access control is part of the design, not a bolt-on.
- Not measuring: without testing answer accuracy against real cases, you have no idea whether the system actually works.
Steering clear of these is exactly what sets apart the RAG systems César García Cabeza builds, where chunking, permissions and evaluation are treated as part of the design from day one.
What are the options for querying internal documentation?
Not every problem calls for a custom RAG. Before deciding, it pays to weigh the alternatives with their real pros and cons:
| Option | Pros | Cons |
|---|---|---|
| Classic keyword search | Cheap, already built into many wikis; quick to switch on | Doesn't understand natural language; returns documents, not answers; struggles with synonyms and indirect questions |
| Uploading documents to a public ChatGPT | No development needed; handy for one-off tests | Risk of leaking confidential data; size limits; no permission control and no reliable citations |
| Turnkey SaaS RAG platform | Fast to deploy; maintenance handled for you | Limited fit with your own workflows and permissions; recurring per-user cost; your data lives with a third party |
| Custom RAG (built into Enclave) by César García Cabeza | Tailored to your documentation, permissions and evaluation; cited, verifiable answers | Needs a larger upfront investment; worth it when knowledge is critical and scattered |
The right choice depends on the volume of documentation, the sensitivity of the data, and how much it matters that answers can be verified.
How is it different from a private ChatGPT?
They are related and often complementary pieces. A private ChatGPT is a broad conversational assistant over your data; RAG is the specific technique that lets it answer precisely, with citations, from your documentation. In practice, a good private ChatGPT usually runs RAG under the hood, as with Enclave, the private ChatGPT by César García Cabeza.
If your main need is to query documentation with cited answers, that is exactly the focus of the internal documentation query service.
In short
RAG connects an LLM to your documentation to deliver precise, cited and verifiable answers, keeping the AI from making things up. Its success rests as much on the technique as on the quality of the source documentation.
Want your team to query your documentation with AI? Book a diagnosis and we'll map it out together.