Use RAG when answers must come from your own documents, stay current, and cite a source, which covers most business use cases. Fine-tuning changes how a model writes, and a long-context model helps for one large document, but neither keeps answers fresh or traceable the way RAG does.

RAG lets you add, change, or remove documents without retraining, so the system stays current as your content changes. When you need to build a RAG chatbot that answers from a living knowledge base, RAG is almost always the right core, because it re-indexes in minutes rather than retraining for days.

In practice, the approaches combine: RAG for grounding, fine-tuning for tone or format, and a long context window when a single document is huge. We help you pick the mix during the audit, and we are honest when RAG is the wrong tool for the job.

Yes. We deploy RAG on private or open models inside your own cloud, or on-prem, so sensitive documents never leave your environment. This lets regulated, security-conscious teams adopt it without sending data to a public API.

We run open models like Llama or Mistral, or a private instance of a commercial model, matched to your quality and compliance needs. Retrieval, embeddings, and generation can all stay inside your walls, and we document exactly where data flows so your security team can sign off with confidence.

You keep full control of the data, logs, and access rules, which usually unblocks legal and security, and we can air-gap the whole system when policy requires it. Our RAG as a service option can run entirely inside your environment, so you get the convenience of a managed system without sending a single document outside your walls.

We measure a RAG system with an evaluation set of real questions and known-good answers, scoring both retrieval and answer quality. Retrieval metrics check whether the right passage came back, and answer metrics check whether the response is grounded, correct, and well cited.

Together, these show where a wrong answer went wrong, so you fix the real cause instead of guessing. Our custom RAG development services track retrieval precision, answer faithfulness, and citation accuracy over time, and we review them with you so the numbers mean something to the business.

This keeps a system honest in production: we watch the same metrics live, so quality does not quietly drift as your documents and questions change. When users can rate or flag an answer, that feedback goes straight back into the evaluation.