How to Train AI on Your Own Business Data

How AI Training With RAG Works

When people say "train AI on your data," they usually mean something different from training a neural network from scratch. You are not building a new AI model. Instead, you are giving an existing model access to your information so it can reference your content when generating answers. This approach is called Retrieval Augmented Generation, or RAG.

The process has three stages. First, your content is broken into small chunks of text, typically a few hundred words each. Second, each chunk is converted into a vector embedding, a mathematical representation that captures the meaning of the text. These embeddings are stored in a searchable index. Third, when someone asks the AI a question, the system finds the chunks most similar in meaning to the question, includes them in the prompt alongside the question, and the AI generates an answer based on that specific information.

This is fundamentally different from fine-tuning, where you actually modify the model's weights. RAG is faster, cheaper, easier to update, and gives you full control over what the AI knows. You can add, remove, or update information at any time without retraining anything. For the vast majority of business use cases, RAG is the right approach. See What Is RAG and How Does It Work for a deeper explanation.

What Data You Can Use

Almost any text-based business content works as training data. The most common sources include:

• Website content: Product pages, FAQ sections, blog posts, documentation. You can crawl your entire website automatically and the system indexes every page.
• Documents: PDFs, Word documents, text files. Upload them directly and the system extracts and chunks the text. See How to Train AI on PDFs and Text Files.
• Support history: Past support tickets, email threads, chat transcripts. This teaches the AI how your team actually handles questions. See How to Train AI on Customer Support History.
• Product catalogs: Specs, pricing, feature lists, comparison charts. The AI can then answer detailed product questions. See How to Train AI on Product Catalogs.
• Internal knowledge: Company policies, onboarding materials, process documentation. Useful for internal AI assistants. See How to Train AI on Internal Company Knowledge.

The key requirement is that the content needs to be accurate and specific. Vague marketing copy does not make good training data. The more concrete and detailed your content is, the better the AI will answer questions about it.

The Training Process

Training your AI through the platform takes three steps. First, choose your input method: upload files, paste text directly, or enter a website URL to crawl. Second, the system automatically chunks your content into appropriate pieces and generates embeddings at 3 credits per chunk. A typical 50-page website might produce 200-400 chunks, costing under $1.20 total. Third, connect the trained knowledge base to your AI Chatbot or any other application that needs to reference your data.

You can add more content at any time. New uploads get chunked and embedded alongside your existing knowledge base. If information changes, you can delete old chunks and upload updated content. The AI always works with whatever is currently in the knowledge base, so keeping it current is simply a matter of uploading fresh content when things change. See How to Keep Your AI Training Data Up to Date.

Keeping Your AI Accurate

The most common concern with AI training is accuracy. Will the AI make things up? Will it give wrong answers? The answer depends almost entirely on the quality of your training data and how well you organize it.

RAG significantly reduces hallucination because the AI is answering from your specific documents rather than generating from its general training. When the system retrieves the right chunk of information, the AI almost always gives an accurate answer. Problems occur when the relevant information is missing from the knowledge base, the chunks are too large or too small to provide useful context, or the content itself is contradictory.

Best practices for accuracy: keep chunks between 250 and 2,000 characters, write clear and specific content rather than vague overviews, remove outdated information that contradicts current facts, and test the AI with real questions your customers actually ask. See How to Improve AI Accuracy With Better Training Data for detailed guidance.

What It Costs

Training costs are based on the number of text chunks processed. Each chunk costs 3 credits to embed (about $0.003). A small business website with 20 pages might produce 80-150 chunks, costing 240-450 credits total. A large knowledge base with hundreds of documents might cost a few thousand credits, still under $5.

You pay the embedding cost once per chunk. After that, the knowledge base is available for unlimited queries. The per-query cost comes from the AI model responding to questions, which depends on which model you choose and the length of the conversation. See How Much Does It Cost to Train AI on Your Data for a complete cost breakdown.

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