> For clean Markdown of any page, append .md to the page URL. > For a complete documentation index, see https://docs.ada.cx/docs/knowledge/core-workflows/knowledge-setup/llms.txt. > For AI client integration (Claude Code, Cursor, etc.), connect to the MCP server at https://docs.ada.cx/_mcp/server. # Knowledge setup ## Overview Getting ready to create automatically generated content from your knowledge base for the first time? Or maybe you're just looking to tune up your knowledge base? Follow these principles to make the information in your knowledge base easy for your AI Agent to parse, which will improve your AI Agent's chances of serving up relevant and helpful information to your customers. ## Content structure\[#UUID-9d2e534b-9f12-97fa-a9ec-cf438f036d0e\_section-idm4610629762313633762904455055] When you're maintaining a knowledge base, it can be easy to organize information in a way that makes sense to you, but not to people who aren't familiar with the information in it already. If you watch a new customer trying to navigate your knowledge base, you'll almost certainly be surprised at what they do! If you're like most people who maintain knowledge bases, you probably aren't a beginner, which means that you likely aren't your own target audience. So how can you make sure your knowledge base is useful for customers, and what does that have to do with AI? The answer here comes down to using **titles** and **headings**. These are referred to as **signposts** collectively, because they act as signposts for both humans and AI, to indicate how likely it is that a customer is getting closer to the information they want to get to in your knowledge base. Additionally, when Ada ingests your knowledge base content, it saves the topic title as context for each chunk of information it splits your knowledge base into. When information has proper context, it's less likely that your AI Agent will serve irrelevant information to customers. * **Categorize your information into groups that don't overlap.** That way, both your human customers and your AI are less likely to make a wrong turn and find information that isn't relevant to what they're looking for. * **Make every signpost relevant to all of the information under it.** If there's information under a heading that isn't relevant to that heading, both customers and AI might have trouble finding that information. Similarly, if the heading is confusing or implies that it's followed by information that isn't actually there, it makes it harder for customers and AI to navigate your knowledge base. * **Always organize information from most broad to most specific.** It should be easy for customers to figure out whether they're getting closer to the information they're looking for by following your signposts. * **Make your signposts descriptive.** * Orient signposts around customer objectives. Most likely, customers are coming to your knowledge base or AI Agent looking for help with a specific task, so making it clear which articles are about which tasks is very helpful. As a best practice, use verbs in your signposts to make it easier for customers to find the actions they want to perform. * To help people and AI scan your content more easily, put important verbs and vocabulary closer to the front of your signposts than to the end. * Wherever you can, avoid mentioning concepts or terminology that new customers might not be familiar with yet. Unfamiliar wording makes it harder for signposts to do their job, because both people and LLMs can find them confusing. * Try to make it easy for customers to figure out whether they're the intended audience for an article just from reading the signposts. No customer wants to waste their time opening articles just to find that they're not relevant, or reading irrelevant responses. * **Use proper HTML structure to create signposts.** It might look just as good if you highlight some text, increase the size, and make it bold, but an AI model might struggle to recognize that that formatting is supposed to indicate a heading. Instead, use the appropriate `

` tags, and so on. When you do, your formatting will be much more consistent, and AI can pick out the hierarchy your information is in. Additionally, your customers who have visual impairments can navigate properly formatted content more easily, because their assistive technology (e.g., screen readers) are programmed to parse HTML. * **Don't assume that customers are going to read your knowledge base in order.** Customers might find an article, or even a section of an article, via a search engine, and might get frustrated if the information they see requires a lot of context they don't have. Likewise, if your AI Agent sends a customer information without context, that can be a frustrating chat experience too. Make sure you lay some groundwork in your more advanced articles so all of your customers can go back and get more information if they need to. The study of organizing information to aid customer navigation is called **information architecture**. If you're interested in more information, including resources on how to perform tests to see how your knowledge base organization works for new customers, see [Information Architecture: Study Guide](https://www.nngroup.com/articles/ia-study-guide/) at the Nielsen Norman Group's website. ### Write standalone content\[#UUID-9d2e534b-9f12-97fa-a9ec-cf438f036d0e\_section-idm4585938791220833916926910122] When an AI ingests the content in your knowledge base, it breaks the information up into chunks. Then, when customers ask your AI Agent questions, your AI Agent searches for chunks that have relevant meanings and uses them to create responses. Here are some ways you can ensure each chunk makes sense on its own: * **Provide information in full sentences.** Because your AI Agent puts information into chunks, the best way to make sure your information has full context is to provide full sentences. For example, let's say your knowledge base contains a FAQ, and one of your questions is "Can I pay by credit card?" Instead of a simple "Yes," which isn't helpful on its own, phrase the answer as "Yes, you can pay by credit card." * **Avoid references to other locations in your knowledge base.** When customers are reading your knowledge base content in the context of a AI Agent, they won't have context for references like "As you saw in our last example." Avoid these kinds of references that make customers feel like they're missing out on information. ### Write clearly and concisely\[#UUID-9d2e534b-9f12-97fa-a9ec-cf438f036d0e\_section-idm4653398621256033763280654432] With information organization covered, the next consideration is what the information itself should look like. The simpler your content is, the easier it is for both humans and AI to find the important pieces of information they need. * **Use clear terminology that doesn't overlap.** The easier your terminology is to follow, the more likely it is that a customer or AI can recognize whether content is relevant to a customer's question. For example, let's say your company makes music publishing software, and your knowledge base has some information about making a demo. But your knowledge base might also have information about how prospective customers can contact your Sales team for a demo of your software. The word "demo" meaning two different things in your knowledge base can cause confusion and cause irrelevant search results to come up. If you can, see if you can replace one instance with a different word, so the word "demo" consistently means only one thing in your knowledge base. * **Use simple language.** Have you ever read a really long, meandering sentence, and by the end weren't really sure what the author was trying to say? This can happen when either human customers or an AI are parsing your knowledge base. Take the time to cut unnecessary content so it's easier to pick out the takeaways from your content. * **Minimize your reliance on images and videos.** Generative content only works with text; your AI Agent can't access images in your knowledge base. If you have content in images, it's a good idea to re-evaluate if there's a way to provide that same content in text. Another reason this is a good idea is for accessibility: your customers who have visual disabilities may not be able to see your images or videos. Making as much text available in text as possible, like in alt text or transcripts, helps both customers chatting with your AI Agent and your customers who access your knowledge base using assistive technology like screen readers. * **Verify your table content.** Some tables work better than others with AI; sometimes AI is able to parse the spatial relationships between cells, and sometimes it gets confused. After setting up your AI Agent, test its ability to provide information that comes from tables in your knowledge base. Your tables are likely to work if they're formatted using proper Markdown, but note that they won't work if they're embedded in images. ## Data-driven maintenance\[#UUID-9d2e534b-9f12-97fa-a9ec-cf438f036d0e\_section-idm4550426331680033785813801902] It's common for documentation teams to be small and sometimes struggle with keeping entire knowledge bases up to date. If you're on a team like that, the idea of turning over your knowledge base to an AI Agent can feel daunting. You're not alone! If this situation feels familiar to you, it's important to work smarter and not harder by following some best practices: * **Collect analytics data for your knowledge base.** There are lots of ways to track usage data for your knowledge base, depending on the tools you use to make it. Customer usage data is often surprising to people who spend all day using a product - that's why it's important to collect it. * **Decide which metrics best indicate success for your knowledge base.** The metrics in your analytics data can be tricky: the stories they tell are often up to interpretation. For example, if customers only tend to spend 10 seconds on a long topic, is it because they tend to be looking for a crucial piece of information near the top of the page? If that's the case, you probably don't need to change anything. But what if they're spending that time scanning through the page for information that isn't there and leaving in frustration? In that case, there's probably something you can improve about the way your knowledge base is organized. There's no right or wrong set of metrics to focus on. It's common to focus on the topics that are most commonly viewed, or topics that have the most positive or negative reviews from customers, but ultimately it's up to you and your organization to choose how to measure the success of your knowledge base. That strategy can change over time, but you should have some data that you can refer back to. * **Prioritize content reviews based on the data you collect.** After collecting some customer usage data, start to go through it. Can you find patterns about the kinds of content that customers seemed to gravitate towards, or other content that customers didn't touch at all? Using your usage data, start creating a priority list for important topics to make sure they're polished. * **You can always disable articles from the Knowledge page.** If you know that a topic is out of date, but it's too low on your priority list to get to right away, you can disable it from showing up in generative content. That way, you can prevent inaccurate information from appearing in your AI Agent, without delaying your launch. In the future, when you do get a chance to update that topic, you can enable it again. * **Revisit your data on a regular basis.** After connecting your knowledge base to your AI Agent, you'll have even more usage data from your customers to analyze. When you revisit your data, you can test the success of your prior decisions and adjust your priorities accordingly. * **Make use of Ada's reporting tools.** On your Ada dashboard, you can see high-level reports on your AI Agent's automated resolution rate, and dig deeper into individual conversations to see how your AI Agent performed. Once your AI Agent is customer-facing, you'll have even more information on how your knowledge base is serving your customers through generative AI. If you're just getting started and don't have analytics yet, consider prioritizing a few key areas of your product to review first, and go from there. You don't have to have a perfect plan for collecting analytics data right away! The important thing is that you eventually have a system where you can both collect and analyze usage data. ## Ongoing improvement\[#UUID-9d2e534b-9f12-97fa-a9ec-cf438f036d0e\_section-idm4495177675051233908602928966] What do you do once you have customer data? You use it! Here are a few tips: * **Keep maintenance regular.** As your product changes, so will your documentation, and so will your customers' questions. Set aside regular maintenance time to take a look at your AI Agent's reports and conversation transcripts, so you can pick out opportunities to improve your knowledge base and have your AI Agent performing even better for future customers. * **Keep feedback loops tight.** If you see an opportunity to improve your knowledge base so you can improve your AI Agent, make that change right away, so you can improve your AI Agent's performance right away. Over time, with the information about how your customers interact with your AI Agent, you'll be able to settle into a workflow where you can improve both your knowledge base and your AI Agent all at once. --- Have any questions? Contact your Ada team, or email us at [](mailto:help@ada.cx?subject=Help%20Docs%20inquiry). ## Docs - [Best practices](https://docs.ada.cx/docs/knowledge/core-workflows/knowledge-setup/best-practices.md)