A RAG chatbot builder that needs no data team

What RAG actually does

A plain chatbot answers from whatever it learned during training — general knowledge, often outdated, and none of it specific to your business. A RAG system adds a lookup step: it finds the passages in your material that match the question and gives them to the model as context.

The model then writes an answer based on those passages. That is why RAG bots can quote your pricing, your refund policy and your setup steps correctly, while a generic assistant cannot.

What GrillMe handles for you

Everything that normally needs an engineer happens automatically when you upload a file.

• Documents are split into passages that can be matched against a question.
• Each passage is indexed so the right section is found, not the whole file.
• Relevant passages are pulled into the prompt with your response guidelines.
• The reply is written from that context — and the visitor never sees the plumbing.

Why not just fine-tune a model?

Fine-tuning changes how a model writes, not what it knows. Your prices still drift out of date, retraining costs money every time a document changes, and nobody can audit what the model actually learned.

With retrieval, the source of truth stays in your documents. Update a file and the next answer uses it — no retraining, no version to track, nothing to explain to an auditor.

Bring your own model

GrillMe works with any OpenAI-compatible provider, so you can point it at the API key and model you already use — or a private endpoint inside your own infrastructure. The retrieval layer stays the same either way.

Frequently asked questions

What is RAG in plain terms?

Retrieval augmented generation: before answering, the system retrieves the relevant passages from your documents and passes them to the model as context. The answer is written from those passages instead of the model’s memory.

Do I need a vector database or embeddings setup?

No. Indexing and retrieval happen when you upload a file. There is nothing to provision, configure or maintain.

Can I use my own OpenAI-compatible API key?

Yes. Bring your own OpenAI-compatible provider and GrillMe uses it for every answer while keeping the same document retrieval.

What happens when a document changes?

Upload the new version and the chatbot works from it immediately. Retrieval means there is no model to retrain.

Is RAG better than fine-tuning for support content?

For content that changes and must be auditable, yes: retrieval keeps answers tied to current documents, while fine-tuning only shapes style and goes stale as facts move on.