This is the full process for training a chatbot on your own data — gather your sources, let it index them, then check its answers before anyone else sees them. The steps hold regardless of which tool you use; below is exactly how it works in EmbedMyBot.
The model matters less than what you feed it. These four things separate a chatbot that gives sharp, trustworthy answers from one that guesses.
Clear headings, one topic per section, plain language. A well-structured document or help page gives the model clean chunks to cite instead of a wall of unbroken text.
Pull from support tickets, sales calls, and your FAQ page. If a question comes up weekly and isn't answered anywhere in your source material, the chatbot can't answer it either.
Outdated source content is the most common cause of a wrong answer. Re-crawl your site and swap stale documents on a regular cadence, not just once at setup.
Policies and spec sheets often live in PDFs while pricing and product details live on your website. Mixing both source types gives the chatbot the fuller picture.
EmbedMyBot builds its knowledge from the documents and pages you give it, then answers strictly from that material.
Upload PDF, Word, Markdown, or plain text files — policies, spec sheets, price lists — and each one becomes part of the chatbot's knowledge within minutes. It's what lets people chat with your PDFs directly, not just your website.
Point it at your domain and it follows your sitemap, indexing pages the same way a search engine would — so training isn't limited to what you manually upload.
A language model powers the conversation underneath, but every reply is checked against your trained sources before it reaches a visitor.
Train one chatbot on your product docs and another on internal policy — each workspace can hold multiple chatbots, each with its own training data.
Once indexing is done, paste a single script tag into your site or share a direct link — no separate publishing step, no developer required.
Analytics surface the questions visitors actually asked, so you know exactly which document or page to add next.
This is the same process no matter which tool you use. Here's exactly how it plays out in EmbedMyBot.
List the documents and web pages that actually answer customer questions — help docs, PDFs, pricing pages, policies. A shorter, accurate set beats a large, messy one.
Drop your files in and point the crawler at your site. EmbedMyBot chunks and indexes everything automatically — most document sets finish in minutes.
Ask it what your customers actually ask, then check the citation on each answer. A shaky citation is a sign the source material needs work — fix it before the chatbot goes live.
Every trained chatbot comes with a way to see exactly which source backs each answer, so you can catch a wrong citation before a visitor ever does.
Nobody publishes a chatbot on faith. Here's what a few teams checked before they went live.
"We uploaded our policy PDFs and crawled our help center on day one. Walking through the setup step by step made it obvious which documents were outdated before a single customer ever saw a wrong answer."
"What sold me was the citation check. Before we published, I could see exactly which page backed every test answer — that's the difference between shipping something you trust and something you're hoping works."
"Training it took an afternoon: our onboarding docs plus a crawl of our support site. Mixing both source types meant it could answer questions neither one covered on its own."
Upload your documents, crawl your site, and test it with real questions before you publish — free to start.