How to Train a WhatsApp Chatbot with Your Company Information

One of the most common fears when a company considers implementing a WhatsApp chatbot is that the bot will respond with generic, incorrect or irrelevant information. And it's a justified fear: a chatbot that doesn't know your products, pricing, policies and internal processes is not only useless, but can damage your customers' trust and your business reputation.

The good news is that modern chatbots with artificial intelligence can be trained with your company-specific information to respond with the same accuracy as your best salesperson. You don't need to be a programmer, you don't need machine learning knowledge and you don't need weeks of configuration. In this article we explain step by step how WhatsApp chatbot training works, what types of documents you can use and what are the best practices to get exceptional results.

What It Really Means to "Train" an AI Chatbot

When we talk about training an enterprise chatbot, we don't mean the traditional training of machine learning models that requires thousands of data, expensive GPUs and teams of data scientists. The concept is much simpler and more accessible.

RAG: Recovery-Augmented Generation

The technology that makes it possible to train a chatbot with your company's information is called RAG (Retrieval-Augmented Generation). In simple terms, it works like this:

1. You feed the system with your documentsYou upload PDF files, website URLs, product catalogs and other relevant documents.
2. The system processes and organizes the information.The platform divides your documents into fragments, analyzes them semantically and stores them in a specialized knowledge base.
3. When a customer asks a questionThe chatbot searches your knowledge base for the most relevant snippets for that specific question.
4. Generates a contextualized responseUsing the information retrieved from your documents, the chatbot formulates a precise, natural and personalized response.

The fundamental difference with a generic chatbot is that a RAG-trained chatbot responds based on real information about your company, not general internet knowledge. If a customer asks "how much does the premium plan cost?", the chatbot consults your updated pricing document and responds with the exact figure, not a generic estimate.

The difference with rule chatbots

Traditional rule-based chatbots require you to manually program each possible question and its corresponding answer. This creates several problems: they don't understand variations in the way questions are asked, they can't combine information from different sources and any updates require manual reprogramming.

A RAG-trained chatbot, on the other hand, understands the intent behind the question regardless of how it is phrased. A customer may ask "what shipping options do you have?", "how do I get my order?" or "do you ship to the province?" and the chatbot understands that all of these questions relate to your shipping policy and responds appropriately using the information you provided.

Types of Knowledge Sources to Train your Chatbot

Not all documents are the same or serve the same purpose. Each type of knowledge source brings a different value to your chatbot. Here we explain which ones you can use and what each one is ideal for.

PDF documents

PDF files are the most versatile and common source of knowledge. You can upload virtually any document your company already has in this format:

• Product CatalogsWith descriptions, technical specifications, materials, dimensions and prices of each product.
• User manuals: Instructions for use, maintenance, troubleshooting and warranties.
• Price lists: Updated rates, volume discounts, payment plans and current promotions.
• Internal policiesReturn, warranty, shipping, cancellation and refund policies.
• Procedure guidesProcesses for quotations, special orders, claims and technical support.
• Commercial brochuresSales material with value propositions, success stories and differentiators.

Practical example: A furniture company uploads its 200-page PDF catalog. When a customer asks via WhatsApp "do you have wooden desks for office?", the chatbot searches the catalog, finds the available desks and responds with names, prices, dimensions and materials for each relevant option.

2. URLs and Web Pages

Your website probably already contains a lot of valuable information that the chatbot can take advantage of. By providing specific URLs, the system crawls and extracts information from those pages:

• Products or services pageDescriptions, benefits and characteristics of what you offer.
• Pricing page: Plans, rates and comparisons between options.
• Blog and informative articlesEducational content that solves common doubts of your customers.
• Frequently asked questions pageOfficial answers to the most common questions.
• Terms and ConditionsLegal information and policies that customers need to know.
• About us" pageHistory, mission, values and team of the company.

Practical example: A gym provides the URL of its membership page. When a prospect asks "how much does the monthly membership cost?", the chatbot pulls the updated prices directly from the website and presents the available options with their benefits.

3. Structured Product Catalogs

For companies with large inventories, structured catalogs allow the chatbot to do precise searches by category, price, availability and features:

• Product name and description: Clear and complete information on each item.
• Prices and variantsBase price, discounts, sizes, colors or configurations available.
• Categories and subcategoriesLogical organization that facilitates conversational navigation.
• Reference imagesURLs of images that the chatbot can share with the customer.
• Stock and availability: Updated stock information.

Practical exampleA clothing store uploads its structured catalog with 500 products. A customer types "I am looking for red dresses size M for party". The chatbot filters the catalog and displays only the dresses that meet all the criteria, with photo, price and availability.

4. Frequently Asked Questions Documents

FAQ's are one of the most effective sources because they represent exactly the questions that your customers have most frequently:

• Questions about the purchase processHow to order, payment methods, processing times.
• Shipping questionsGeographical coverage, delivery times, shipping costs, package tracking.
• Questions about warranties and returns: Deadlines, conditions, process for requesting a change.
• Technical questionsCompatibility, requirements, specifications and solutions to common problems.
• Questions about the company: Hours of operation, location of branches, contact information.

5. Guidelines and Internal Documents

Information that normally only your sales or support team knows, but is valuable for serving customers:

• Sales scriptsCommercial arguments, objection handling and closing techniques.
• Troubleshooting GuidesSteps to diagnose and resolve frequent incidents.
• Relevant internal processesApproval flows, escalation and response times.
• Competitive informationDifferentiators against the competition and key advantages of your products.

Step by Step Guide: How to Train your Chatbot in Aurora Inbox

Aurora Inbox offers a chatbot training feature designed to allow any company to set up their virtual assistant without any technical knowledge. Here we explain the complete process.

Step 1: Collect and Organize Your Information

Before uploading documents, take the time to organize the information you want your chatbot to know:

• Identify frequently asked questions your sales and support team receives.
• Gather existing documents that already contain the answers to these questions.
• Verify that the information is up to date: current prices, available products, current policies.
• Eliminates contradictory informationIf you have several versions of a document, use only the most recent one.

Step 2: Upload your PDF Documents

In the Aurora Inbox dashboard, access the chatbot training section and upload your PDF files:

• Drag and drop files or select them from your computer.
• The system processes each document automatically, extracting the text and organizing it semantically.
• You can upload multiple documents on different topics; the system integrates them into a single coherent knowledge base.
• Documents with good structure (titles, subtitles, lists) are processed more accurately.

Step 3: Add URLs of your Website

Provide the URLs of the pages of your website that contain relevant information:

• Enter each URL you want to include in the knowledge base.
• The system crawls the page, extracts relevant content and discards navigational and advertising elements.
• You can add as many URLs as you need to cover all your business information.
• The pages can be updated periodically to keep the information up to date.

Step 4: Configure your Product Catalog

If you have a product catalog, configure it so that the chatbot can make precise recommendations:

• Upload your catalog with names, descriptions, prices and categories.
• Define the relevant variants for each product (sizes, colors, models).
• Include reference images for the chatbot to share in the conversation.
• Update the catalog whenever there are changes in your inventory or prices.

Step 5: Test and Adjust

Once the information is loaded, it is essential to test the chatbot before putting it into production:

• Ask test questions simulating different types of customers and inquiries.
• Verify accuracy of the answers by comparing them with the information in your documents.
• Test limiting casesAmbiguous questions, discontinued products, changed prices.
• Adjusts documents if you detect incorrect or incomplete answers.
• Test in different languages if you serve customers who speak different languages.

Best Practices for Training your Chatbot

Following these recommendations will help you get the best results from your trained chatbot.

Keeping information up to date

A chatbot is only as good as the information it is trained with. If your prices change and you don't update the documents, the chatbot will give incorrect prices. Establish an update routine:

• Weekly: Check prices, promotions and product availability.
• MonthlyVerify that policies and processes are still in force.
• Immediate: Updates when there are significant changes (new products, policy changes, special promotions).

Use structured formatting in your documents

Well-organized documents produce better results:

• Use clear headings and subheadings that describe the content of each section.
• Organize information into lists where possible (specifications, steps, requirements).
• Avoid excessively long paragraphs; divide the information into digestible blocks.
• It includes the textual questions that customers usually ask and their answers.

Review conversations regularly

Periodic review of conversations between chatbot and customers is a gold mine for improving training:

• Identify questions that the chatbot could not answer and add that information to the documents.
• Detects inaccurate or incomplete answers and corrects source documents.
• Discover new frequently asked questions that you had not considered.
• Observe patterns in the way customers formulate their queries.

Testing extreme cases

Don't just test the obvious questions. Simulate complicated scenarios:

• Questions about products that do not exist in your catalog.
• Consultations combining multiple topics (price + shipping + warranty).
• Messages with spelling errors or informal language.
• Requests requiring human intervention (complaints, complex situations).

Common Mistakes When Training a Chatbot and How to Avoid Them

Error 1: Uploading outdated information

The problemYou upload a catalog from last year with old prices. Customers receive incorrect quotes and lose confidence in your business.

The solutionBefore uploading any document, verify that it is the most recent version. Establish an update schedule and assign a responsible person.

Pitfall 2: Documents with contradictory information

The problemYou have a PDF that says free shipping above $500 and another document that says free shipping above $1000. The chatbot does not know which information to use.

The solution: Audit all your documents before uploading them. Remove old versions and make sure there is only one source of truth for each topic.

Pitfall 3: Lack of information on service limits

The problemChatbot doesn't know what to do when asked a question that is out of your company's scope. It invents answers or gives incorrect information.

The solutionInclude in your documents explicit information about what your company does not do, the areas you do not cover, the products you do not handle and when the conversation should be transferred to a human agent.

Mistake 4: Not testing before launching

The problemYou put the chatbot into production without testing it properly. The first customers receive incorrect answers and a bad impression is generated.

The solutionSpend at least one week on internal testing. Involve different members of your team to ask questions from different perspectives.

Mistake 5: Never revise and improve

The problemYou set up the chatbot once and forget about it. Over time, the information becomes obsolete and the quality of responses deteriorates.

The solutionSchedule monthly reviews of conversations, update documents regularly and continually improve based on real customer feedback.

Results You Can Expect

Companies that properly train their WhatsApp chatbot with Aurora Inbox report significant results:

• 60-80% reduction in repetitive questions that used to consume your team's time.
• Immediate answers 24/7 regardless of the time or day of the week.
• Increased accuracy of information shared with customers, as it comes directly from your official documents.
• Increase in sales conversion because prospects receive accurate information instantly, without waiting for an agent to be available.
• Improved customer experience with personalized and relevant answers instead of generic answers.

Frequent questions

Do I need technical knowledge to train the chatbot?

No. The Aurora Inbox training process is designed for non-technical users. You just need to upload your PDF documents or provide your website URLs. The platform takes care of all the technical processing, semantic indexing and AI model configuration. If you know how to attach a file to an email, you can train your chatbot.

How long does it take for the chatbot to be ready after uploading the documents?

Document processing is fast. PDF files and URLs are processed in a matter of minutes depending on their size and quantity. Once processed, the chatbot can start answering questions based on that information immediately. However, we recommend spending a few days testing and tuning before contacting real customers.

What happens if the information on my documents changes?

You can update your documents at any time. Simply upload the new version of the document or update the corresponding URL. The system reprocesses the information and the chatbot starts using the updated data. It is advisable to establish a regular update routine to ensure that the information is always current.

Can the chatbot answer questions that are not in my documents?

The chatbot is designed to respond based on the information you provide. If a customer asks a question whose answer is not in your documents, the chatbot can indicate that it does not have that information and offer to transfer the conversation to a human agent. This is preferable to making up an answer, as it protects your company's credibility.

Can I train the chatbot with information in different languages?

Yes, the AI system can process documents in multiple languages and respond in the language in which the customer is writing. This is especially useful for companies that serve customers in different countries or regions. You can upload documents in Spanish, English, Portuguese or other languages and the chatbot will handle the queries in the corresponding language.