Fuel your AI chatbot’s success. The ultimate guide to data preparation.

Generative AI chatbots are rapidly transforming the way we interact with businesses and access information. At KODA.AI, we create chatbots that power meaningful conversations. But just like any engine, their performance relies on high-quality fuel – data. This guide outlines the essential stages for data preparation. With each stage, you’ll gain the tools and expertise to build a solid foundation for your chatbot’s success.

Set the course – define objectives

The first and most important step in preparing data for your AI chatbot is to define your objectives. Understanding the problem you aim to solve and the outcomes you desire will set the foundation for your entire data preparation process. Consider questions such as:

  • Primary Function: What is the main purpose of the chatbot? (e.g., customer support, sales assistance, knowledge management)
  • Specific Goals: What do you hope to achieve? (e.g., reduce response time, improve customer satisfaction, increase conversion rates)
  • Target Audience: Who will be interacting with your chatbot? (e.g., existing customers, potential leads, general public, employees)

Defining your objectives aligns your data preparation efforts with your business goals, ensuring the chatbot’s functionality meets your specific needs

Collect key data – building blocks of conversation

Once you have a clear objective, it’s time to assemble the building blocks of your chatbot’s knowledge. This involves a diverse collection of data sources to build your custom knowledge base. Key types of data include:

  • Product Feeds: Detailed information about your products or services, including specifications, features, pricing, and FAQs. This helps the chatbot provide accurate and detailed responses to product-related questions.
  • Instructions and manuals: Detailed guides, troubleshooting steps, and how-to manuals that the chatbot can use to assist users with specific problems.
  • Industry-specific terminology: Specialized vocabulary relevant to your field, ensures the chatbot understands and correctly uses industry-specific language.
  • Customer insights: Analyzing past customer interactions to identify common questions, frustrations, and communication styles. Gather insights from customer surveys to uncover preferences, pain points, and frequently asked questions. This data can be used to tailor responses to better meet customer needs.

Collecting a wide range of high-quality data enriches your chatbot’s knowledge base, enhancing its ability to respond accurately and effectively.

Shape your data

The format of your data significantly impacts how effectively the AI can understand and utilize it. Here are the best formats:

  • Readable text formats: For documents, plain text formats like .docx or .doc are ideal, as they are easily readable by AI. PDFs can be tricky because they can contain text that is embedded in a variety of formats, such as images, vector graphics, or standard text. While AI can extract text from images using Optical Character Recognition (OCR), complex layouts, multiple columns, irregular formatting or image-heavy content can make it difficult to understand the context of the information. 
  • Product feeds: Ensure your product feed is in a usable format (XML, JSON, CSV). If your product feed changes frequently we can perform regular updates for accurate and relevant data.
  • Databases: Our platform also integrates with databases via BigQuery, allowing seamless data extraction.
  • Websites: Websites can be scanned for relevant information, though it’s more reliable to provide text-based formats, especially if a redesign is planned. They also vary widely in their HTML structure and layout, which can complicate the process of extracting relevant text and data for AI chatbots. Inconsistent tagging or nested elements can require complex parsing techniques making it time-consuming to extract useful information.
  • CRM systems: We integrate with CRM systems to download and utilize information, ensuring the chatbot has comprehensive and up-to-date data to work with. Data from these systems can be beneficial for customer service chatbots, especially in e-commerce applications.

Proper data formatting ensures that AI chatbot can access and process information efficiently, leading to better performance and user satisfaction.

Ensure data quality and avoid biases

High-quality data is essential for training a reliable AI chatbot. Data cleansing and validation involve several critical steps to ensure your dataset is relevant, and free from biases. This stage includes:

  • Removing duplicates and errors: Identify and eliminate duplicate entries and correct any inaccuracies in your data to prevent confusion and errors in chatbot responses.
  • Handling missing data: Address gaps in your dataset by either filling them with reliable information or adjusting your dataset to account for missing values.
  • Bias mitigation: Identify and address potential biases in your data that could lead to discriminatory or unfair responses from your chatbot. For example, a customer service chatbot trained on imbalanced data sets might prioritize resolving issues for a specific customer segment. Ethical data practices are paramount here, ensuring your chatbot reflects a diverse and inclusive voice.

Clean, validated data ensures your chatbot operates fairly and accurately, providing reliable assistance to users.

Knowledge base in KODA Platform – example data topics

Organize for efficiency

Think of your data as a library. Proper organization allows the chatbot to quickly retrieve and utilize the right information when responding to user queries. Advanced organization techniques include:

  • Topic categorization: Group data by themes relevant to your chatbot’s domain. For instance, a banking chatbot might have categories like “account management,” “loan applications,” and “security procedures.” This allows the chatbot to quickly locate the most relevant information.
  • Intent-based organization: Classify data based on user intent. This involves grouping information together based on the desired outcome of a user’s query. For example, “troubleshooting steps” for users seeking solutions, or “product comparisons” for users in the research phase. This empowers your chatbot to anticipate user needs and deliver targeted responses.
  • Chronological or contextual order: Organize data based on time-sensitivity or conversational flow. Post-purchase instructions should be categorized chronologically after the purchase confirmation. Similarly, responses related to booking a flight could be grouped by stages of the journey (e.g., departure information vs. baggage allowance). This contextual organization allows the chatbot to maintain a natural flow of conversation.

Clarity and relevance for effective communication

Remember, your chatbot is only as good as the data it’s trained on. Follow this steps to ensure its quality:

  • Clarity and concision: Express information in straightforward language and avoid technical terminology your users might not understand. Imagine you’re explaining a complex topic to someone unfamiliar with the field.
  • Simplifying complexity: Break down complicated concepts into easily understandable chunks.
  • User-centric relevance: Focus on information that directly addresses your target audience’s questions and concerns. Don’t overload your chatbot with details that might overwhelm users.

Continuous improvement through testing and refinement

Data preparation is an ongoing process, not a one-time fix. At KODA, we regularly test our chatbot’s responses against your training data. We analyze performance, identify areas for improvement, and refine your data to enhance its accuracy and understanding. Our key testing strategies include:

  • Scenario-based testing: We develop test cases that simulate real-world user interactions. This helps us to identify gaps in your data and areas where the chatbot might struggle.
  • User feedback: We actively collect feedback from actual users to gain insights into the chatbot’s strengths and weaknesses. 
  • LLM A/B testing: We leverage A/B testing to compare different response versions generated by various Large Language Models (LLMs) like Anthropic’s Claude and OpenAI’s GPT-4o. This data-driven approach helps us optimize the chatbot’s performance for your specific needs.


Great chatbots start with great data. At KODA.AI, we see data as the fuel, and our AI solutions as the engine. Investing your time in data preparation can lead to smarter, more effective chatbot experience. You focus on refining your data, and we’ll handle the rest. Exceptional results are just a conversation away.


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