Training your chatbot is less about uploading everything and more about uploading the right things, structured correctly. A well-trained chatbot delivers accurate, on-brand answers. Before diving into detailed documents, there are a few essentials every chatbot should start with.

Step 1: Start With a Clear Project Description

Every chatbot should have a short, accurate description of your business and what the chatbot is responsible for. This should clearly explain: what your business does, who your customers are, and what the chatbot should help with (support, sales, internal queries, or all three).

Keep this concise. Think of it as context, not marketing copy. This helps the chatbot frame answers correctly and avoid unnecessary assumptions.

Step 2: Include a Link to Your Website

Adding a link to your website tells the chatbot that it is allowed to be used on that website. It essentially blocks other people from using the chatbot should they gain access to the code snippet.

The chatbot does not read information from the site—all training content should still come from your Knowledge Base.

Step 3: Structuring Documents for Your Knowledge Base

The knowledge base is powered by a RAG (retrieval-augmented generation) model and a vector store: your documents are processed, stored there, and retrieved at query time. The documents you upload are the most important piece of training material. This is where most chatbot performance issues are won or lost.

For example structures, see the knowledge base templates on the Resources page.

Step 4: Use Clear Sections and Headings

Structure your documents into clearly labelled sections. Each section should focus on a single topic, such as: products or services, pricing and packages, policies and terms, booking and availability, and common customer questions.

Avoid long, unbroken blocks of text. Clear headings make it easier for the chatbot to retrieve the correct information quickly.

Step 5: Write in Plain, Declarative Language

Knowledge base documents should be factual, not conversational. Use short, direct sentences and avoid marketing fluff. For example: “We offer next-day delivery within the UK.” and “Appointments are available Monday to Friday, 9am to 5pm.”

This reduces ambiguity and improves answer accuracy.

Step 6: Keep Information Atomic

Each paragraph should contain one idea. Avoid mixing pricing, eligibility, and exceptions in a single block.

Breaking information into smaller, self-contained chunks improves retrieval precision and prevents partial or misleading answers.

Step 7: Include Variations of Common Questions

Where possible, include both the information and the way users might ask about it. For example: “What does this service include?” and “Is this covered in the standard package?”

This helps the chatbot match real-world queries to the right content.

Step 8: Use a Single Source of Truth When Uploading Multiple Documents

When you upload multiple documents, treat them as a single source of truth: do not allow contradicting information across files. Conflicting information is one of the biggest causes of poor chatbot performance.

Ensure there is one authoritative version of each rule, price, or policy—multiple files should not contradict each other. If something changes, update it in one place rather than adding a new note elsewhere.

Step 9: Separate Public and Internal Knowledge

If your chatbot is customer-facing, keep internal processes, staff notes, and operational shortcuts in a separate document or restricted section.

This prevents accidental disclosure and keeps responses focused on the right audience.

Step 10: Maintain and Refine Over Time

Your knowledge base documents are living assets. Review chat logs regularly to identify gaps, unclear answers, or repeated questions that are not being handled well.

Small structural improvements compound quickly across all conversations.

Step 11: Summary

A chatbot does not need more data. It needs clean structure, clear intent, and reliable source material. Start with a solid project description and website context, then invest time in well-structured documents for your knowledge base.

That is what turns a chatbot from a novelty into a dependable business tool.