How to train an AI chatbot on your own data

People often ask how to "train ChatGPT on their own data." For customer support, you almost never want to fine-tune a model — you want the AI to retrieve answers from your content and stay grounded in it. Here is how that works and how to set it up.

Fine-tuning vs. retrieval

Fine-tuning bakes patterns into a model and is expensive, slow and easy to get wrong. Retrieval (often called RAG) keeps your content in a searchable knowledge base and feeds the relevant pieces to the model at answer time. For support, retrieval wins: answers stay accurate, update instantly when your content changes, and can cite their source.

1. Gather your sources

The best support agents learn from everything you already have:

• Your website — product, pricing and help pages
• Documents — PDFs, Word files and Markdown (manuals, policies, runbooks)
• Your help center or knowledge base

2. Index, do not fine-tune

With Chatixy you paste your URL and upload your files; it chunks and indexes them into one private knowledge base. There is no training data to label and no model to host — indexing finishes in minutes.

3. Keep answers grounded and cited

Because the agent retrieves from the specific pages and documents you indexed, every answer can link the source it used. When a question falls outside the knowledge base, a good agent says so and hands off to a human rather than inventing an answer.

4. Keep it fresh

Your content changes, so your agent should too. Chatixy re-crawls your site on a schedule, and you can trigger a manual re-crawl right after you publish new docs or change pricing — so the agent never quotes stale information.

Do I need machine-learning skills?

No. The whole point of the retrieval approach is that you bring content, not models. If you can paste a link and upload a file, you can train a support agent on your own data.