Why Your AI Chatbot Fails and How to Fix It

If you're building a RAG chatbot for your business, you've probably already hit the chatbot hallucination problem: the bot confuses topics, gives wrong answers, or flat-out makes up information that doesn't exist. Just uploading documents doesn't work.

The good news: you can use AI itself to prepare your content properly. In this article, we give you the exact prompts to fix chatbot hallucinations step by step.

Why Your RAG Chatbot Gives Wrong Answers

RAG (Retrieval-Augmented Generation) systems work by splitting your documents into small fragments called chunks. When a user asks a question, the system searches for the most relevant chunks and generates a response based on them.

The problem is that:

• Chunks lose context: A fragment might contain information without any indication of what topic it belongs to. The AI sees text but has no idea where it came from.
• Similar topics get confused: "Return policy" and "Exchange policy" sound similar enough that the RAG system retrieves the wrong chunk, leading to chatbot wrong answers.
• Content is full of garbage: Navigation menus, footers, HTML tags, cookie banners — all of this noise confuses the retrieval system and degrades AI chatbot accuracy.

Step 1: Extract All Content from Your Website

The first step to train your chatbot properly is extracting all your content. Models like Claude or ChatGPT can analyze entire websites and extract structured content for you.

Analyze the website [URL] completely. Navigate through all sections and internal pages. Extract all relevant content (text, service descriptions, contact details, FAQs, pricing, policies, etc.). Organize it into logical categories. Ignore navigation menus, footers, and repetitive elements.

Step 1.1: If Your Website Blocks AI Access

Some websites have anti-bot protections, and the AI can't access them directly. Don't worry — if you don't mind feeling like a hacker for five minutes, there's a solution.

Ask the AI to generate a scraping script you can run in your terminal. This script will crawl all your website pages, save the content into text files, and compress them into a ZIP. Then you just upload that ZIP back to the AI and continue with Step 2.

It sounds complicated but it really isn't. You literally copy, paste, and hit Enter. The AI walks you through it step by step.

Prefer not to deal with it? At Bravos AI we can do this for you. We've already cleaned and organized content for several clients. Just reach out and we'll take care of it.

Step 2: Clean Your Training Data for RAG

Web content comes loaded with garbage: residual HTML code, repeated navigation menus, duplicate text blocks, encoding errors. All of this noise is one of the main causes of RAG chatbot hallucination — the system retrieves irrelevant fragments and the AI tries to make sense of nonsense.

Clean this content for use in a RAG system:
1. Remove duplicate paragraphs and sections
2. Remove residual HTML/CSS code and tags
3. Remove repeated navigation menus and footers
4. Fix encoding errors (broken characters, mojibake)
5. Remove sections with no informational value (cookie notices, empty placeholders)

[PASTE YOUR CONTENT HERE]

Step 3: Optimize Titles to Prevent Chatbot Hallucinations

This is the most important step for RAG content preparation. Generic titles are the number one reason chatbots confuse similar topics. When two sections have vague headings, the embedding vectors end up too close together, and the retrieval system grabs the wrong one.

Review these section titles and optimize them for a RAG retrieval system:
1. Identify titles that could be confused with each other
2. Rewrite them with unique, distinctive keywords
3. Make each title descriptive enough to stand alone

Example of what to fix:
- Bad: "Returns" and "Exchanges"
- Good: "RETURN PRODUCT - FULL REFUND PROCESS" and "CHANGE SIZE OR MODEL - EXCHANGE POLICY"

The goal: a RAG system should never confuse one section for another based on the title.

[PASTE YOUR TITLES HERE]

Step 4: Add Context That Survives RAG Chunking

When the RAG system splits your document into chunks, each fragment must be understandable on its own. If a chunk just says "The price is $49/month" without mentioning what product or service it refers to, the chatbot has no way to give an accurate answer. This is a core cause of chatbot wrong answers.

Before applying this step, check what chunk size your application uses. It's usually in the documentation or configuration. For example, at Bravos AI we use 800-character chunks with 150-character overlap. Adapt the prompt below to your specific RAG chunking optimization needs.

Reformat this content for a RAG retrieval system:
1. Add the organization name to the title of every section
2. Add 1-2 introductory context sentences at the beginning of each section (who, what, which product/service)
3. Add context markers approximately every ~500 characters that remind the reader what topic/section they are in

The goal: if someone reads any random 500-character fragment, they should immediately know what company, product, and topic it refers to.

[PASTE YOUR CONTENT HERE]

Step 5: Verify All Links in Your Content

There's nothing worse than a chatbot that confidently shares broken links. If your content includes URLs, you need to verify every single one before uploading it to your RAG system.

Extract all URLs from this content and verify them:
1. List every URL found
2. Check if each URL is properly formatted (valid protocol, no typos)
3. Identify URLs that look suspicious, outdated, or potentially broken
4. Flag any URLs pointing to generic pages (homepages) when they should point to specific pages

[PASTE YOUR CONTENT HERE]

Step 6: Configure Your System Prompt to Stop Chatbot Hallucinations

Even with perfectly prepared content, your chatbot can still make up information if you don't explicitly tell it not to. This is the chatbot hallucination fix that most people skip — and it's the easiest one to implement.

CRITICAL RULES:
1. If you cannot find the information in your knowledge base, say that you don't have that information. NEVER make it up.
2. NEVER invent phone numbers, addresses, prices, schedules, or names. If it's not in your data, don't say it.
3. When you are unsure, it is always better to say "I don't have that specific information" than to guess.

Step 7: Test with Critical Questions to Validate AI Chatbot Accuracy

Before going live, test your chatbot with two types of questions designed to expose problems:

• Confusion questions: Ask about topics that could easily be mixed up (e.g., "What's your return policy?" vs. "What's your exchange policy?"). If the bot gives the same answer for both, your titles aren't differentiated enough.
• Trick questions: Ask about something that is NOT in your knowledge base (e.g., a product you don't sell, a service you don't offer). If the bot makes up an answer instead of saying it doesn't know, your system prompt needs stronger anti-hallucination rules.

Based on this content, generate:
1. 10 confusion questions - questions that could trick a RAG system into retrieving the wrong section (e.g., asking about returns when the answer is in the exchanges section)
2. 5 trick questions - questions about information that is NOT present in the content at all (the correct answer should be "I don't have that information")

Format each question with the expected correct behavior.

[PASTE YOUR CONTENT HERE]

Final Checklist: RAG Content Preparation

Conclusion: Content Preparation Is the Key to Fix Chatbot Responses

Proper RAG content preparation is the difference between a chatbot that frustrates your customers and one that genuinely helps them. Yes, it takes time to do it right. But with the prompts in this guide, you can use AI itself to do the heavy lifting.

If after following all these steps your chatbot still gives wrong answers or makes up information, the problem is probably not your content — it's how the system searches and retrieves information. That's where a properly implemented RAG architecture matters, and it's exactly what we've built at Bravos AI.