1.6 – Creating a Knowledge Base Lesson

Lesson Overview

Building a chatbot that can answer questions with information from your files is a major upgrade over models that can only answer based on general knowledge. In this lesson, you’ll see how to create your own knowledge base by uploading documents to your chatbot’s document library. You’ll organize those documents with tags, making it easy to group, find, and retrieve content later on.

This lesson is a core part of setting up a personalized AI that references materials unique to you—internal documentation, guides, blog posts, or any source relevant to your needs. It positions you to use your chatbot for tasks such as drafting content, searching company policies, or responding with details from technical documentation.

Being able to quickly pull these documents into a chat by searching or using hashtags saves time and ensures more consistent, accurate answers. This capability, sometimes called retrieval-augmented generation (RAG), is especially useful for anyone who needs their AI chat tool to reference specific, local information that’s not available on the web.

Who This Is For

Creating a chatbot knowledge base is helpful if you want quick, direct access to important documents during conversations. You’ll benefit from this lesson if you are:

• An educator maintaining a repository of learning materials
• A business owner or team member centralizing standard operating procedures
• A developer building support bots for technical documentation
• Anyone who wants their AI chatbot to reference personal files
• Professionals needing secure, local-only access to data in chats
• Content creators managing drafts, articles, or blog posts

Where This Fits in a Workflow

Uploading and tagging documents to a knowledge base becomes essential early in a chatbot project. The process prepares your resources for later use—instead of scrambling to find and copy content each time, everything is ready and organized before you need it. For example, you might load onboarding guides and product instructions, then later, while chatting, instantly pull in a relevant file using a hashtag shortcut.

This organization method is also useful in customer support workflows: you can have product manuals, troubleshooting FAQs, and template responses ready for retrieval in live chats, speeding up responses and maintaining accuracy.

Technical & Workflow Benefits

Previously, you may have manually pasted information into a chat, searched through folders, or interrupted your workflow to look up details. With a knowledge base uploaded and tagged, all relevant files are at your fingertips—just call them into chat with a hashtag.

This efficient method reduces time spent on repetitive searches and improves answer consistency. For example, support staff can load the same set of approved responses. Developers or educators can quickly reference technical guides or course documents while discussing projects. The approach also supports using different AI models, allowing you to compare quality and format of responses directly, leading to smarter decisions about which model or source is best for each conversation. By centralizing your resources, your chatbot always has the right information—without sacrificing your data’s privacy and security.

Practice Exercise

Set up a trial knowledge base to see the benefits in action:

1. Choose three different documents you often reference (for example: a company policy, a how-to guide, and a technical FAQ).
2. Upload them to your chatbot’s document library; assign each a unique tag based on its content area.
3. Open a new chat and, using the hashtag shortcut, pull each document into the conversation in turn.

Afterward, compare how the AI responds when referencing each document, and note any differences in formatting or quality based on the source or model selected. Which combination gave you the most useful response?

Course Context Recap

This lesson is a key step in the Private AI Chatbot On Your Computer course, building on earlier sessions about setting up your chatbot environment. Now that you can create your own knowledge base, you’re ready to move forward with advanced tasks—like refining prompt engineering or comparing AI model performance. Continue through the course to explore more about making your chatbot smarter and more responsive to your needs. There’s more to learn as you unlock new ways to use your own data with private AI.