I used to truly despise knowledge bases. Digging through heaps of search results was a pixel-in-a-haystack pursuit that always led to MORE frustration instead of less. Thankfully knowledge bases have gotten a huge glow-up with artificial intelligence (AI).
AI knowledge bases use machine learning (ML) and natural language processing (NLP) to interpret human language and simulate a conversation with a trained professional. Where was this back in 2018 when I was building a website from scratch?! I think it would've saved me a year of sifting through help articles.
Getting instant answers to questions is great for customers and saves customer service teams a lot of time answering repetitive questions. Here's how to leverage this opportunity for yourself, no matter how big your team is.
Table of Contents
What Is an AI Knowledge Base?
An AI knowledge base is a custom generative pre-training transformer (GPT) that's been trained on company knowledge to provide contextually relevant answers to consumers. Users ask questions, and the advanced search functionality delivers a custom response (as opposed to the self-service option of traditional knowledge bases). AI knowledge bases are trained on your existing knowledge base, how-to articles, user manuals, and more.
How is an AI knowledge base used?
Users enter a query, and, instead of sifting through knowledge base articles manually, generative AI offers an answer. The aim is minimal human intervention, but assistance may still be needed if the AI model isn't trained to answer their question.
An AI knowledge base is used as a form of self-service for the public, a resource for a company's internal team, or an add-on for a paid user group.
5 Types of AI Knowledge Base Content
AI knowledge bases can be used by customers OR employees. Here are different types of knowledge bases that your team could be leveraging.
General Internal
A company-wide internal knowledge base catalogs organizational knowledge like company policy, general company information, etc. This is a common form of knowledge management that companies are leveraging to help employees find information more easily. Data sources can include:
- Onboarding materials.
- Company documents.
- Company website.
- Employee roster.
- SOPs.
An employee would use this AI-powered knowledge base to answer questions like, “What’s the company holiday schedule this year?"
Sales
There are many uses for AI in sales that can help sales representatives work more quickly, with one of the most popular tools being an AI-driven knowledge base. This type of AI knowledge base assists sales reps in answering questions and finding resources for potential customers. Some data sources include:
- Company data and research.
- Buyer objection training.
- Product demo videos.
- Sales call scripts.
- Marketing materials.
- Training materials.
- Product manuals.
- Customer emails.
Statistic: 52% of sales professionals say that AI tools are very to somewhat important in their day-to-day role.
A sales rep would use this knowledge base to answer questions like “What are responses to [X] buyer objections?”
Customer Self-service
This type of knowledge base is built for users of your product to help answer customer queries and improve the customer experience. Some data sources include:
- Past customer resolutions.
- Troubleshooting guides.
- Product demo videos.
- Sales call transcripts.
- Product sales pages.
- Training materials.
- Product manuals.
- How-to guides.
- Chatbot logs.
Statistic: 92% of our customer service survey respondents say that AI improves time to resolution.
A customer would use this type of knowledge base to answer questions like, “Where can I find [X] product tutorial?”
FAQ: What about when human intervention is needed to respond to customer queries? This is where human customer support teams come in, but they can still have AI support at their fingertips.
Are you hitting key customer service metrics? Find out with our free Customer Service Metrics Calculator.
Customer Service Support
A customer service support knowledge base is used by human support teams to help them answer customer queries more quickly. This AI knowledge base would be trained on all of the same data sources as the customer self-service knowledge base, and it would also include relevant internal data.
A customer service support knowledge base would be used to answer questions like, “What is the status of [X] order?”
Product Specific
If you have an information-intensive product, such as an online course, you can build a custom knowledge base that's trained to help customers succeed with that product alone.
This tip comes from AI expert Isabella Bedoya: “90% of people that buy courses don't watch the videos, so how can you help them get results? You can create a custom GPT or an AI assistant that's trained on your course content.” Data sources can include:
- Marketing materials.
- Live call transcripts.
- Onboarding emails.
- Video transcripts.
This type of personalized self-service can help improve customer success with a specific product.
Statistic: 78% of surveyed customer service reps agree that customers now expect more personalized experiences than ever before.
Benefits vs. Drawbacks of Using an AI Knowledge Base
These different types of AI-powered knowledge bases have several key benefits for companies:
- Efficiency. No matter how well-trained your company reps are, your AI model will be faster at answering customer queries.
Statistic: 73% of marketers say that AI can help them be more productive in their roles.
- Improved customer support experience. AI knowledge bases can offer faster service to customers, and often more multidimensional service as well. For example, a knowledge base that's trained on multiple language models can automatically detect and switch languages when speaking to customers.
- Budget-friendly. The reduced load on customer support teams will free employees up to focus on more complex tasks.
- Convenient. A traditional knowledge base was a stagnant website page. Now, a knowledge base can be incorporated into the chatbot function that lives on every website page.
Statistic: 79% percent of service pros using AI say that they find it effective.
Here you can see Canva's chatbot automatically detecting my language and immediately answering my query in Dutch:
AI knowledge bases have huge upsides, but there are also potential drawbacks:
- Inaccuracy. Machine learning algorithms aren‘t perfect — if the wrong data is input or your AI model isn’t tested, you deliver inaccurate information to customers.
- Customer isolation. The reduced number of customer interactions may leave some customers feeling under-served (especially older web users, who appreciate one-on-one service).
- Resistance to adoption. A percentage of all employees and customers will resist the use of AI and resist adoption.
How to Build an AI Knowledge Base
AI experts Isabella Bedoya and Chase Fowler, co-founders of Infinite AI, shared their expertise with me on how to build an AI knowledge base (and software recommendations that can get you started in just a few clicks). These are their insights for building your own AI knowledge base.
Step 1: Define your goal.
Who is your knowledge base for, and what are you trying to achieve by implementing it? Your goals need to be established before beginning with data and implementation. To recap, the five popular types of AI knowledge bases are:
- General internal.
- Sales.
- Customer self-service.
- Customer service support.
- Product specific.
Step 2: Find quality data sources.
Every AI knowledge base starts with a root knowledge base that stores all of its information (like a data repository). The type of data that you choose to use will make or break your AI system.
"Inputting quality, relevant data is the most important step," shared Isabella Bedoya. She warned that you can confuse the chat by making it a catch-all chatbot instead of being specific.
"Let‘s say you’re building a sales chatbot — if you start inputting all your company data that‘s not relevant, it’ll confuse the chat. If you say, ‘This chatbot is only for sales,’ and you only equip it with the information and needs to run sales conversations, then it‘s going to perform great. Don’t add company history, etc., when it's not necessary,” Bedoya says.
Here are ideas for gathering data for your custom GPT:
- Forum or Facebook group discussions.
- Support team conversations.
- Employee training materials.
- Past customer interactions.
- YouTube video transcripts.
- Social media interactions.
- Existing knowledge base.
- Company web pages.
- Sales call transcripts.
- How-to articles.
- User manual.
- Help forums.
- Workbooks.
- Chat logs.
- FAQs.
A simple approach: Chase Fowler shared that something as simple as a Notion document can be a data source for your AI knowledge base.
"A Notion document itself can be the knowledge base. If the company already has input information into Notion, it can be connected automatically so that when someone asks a question, it's searching through that specific document,” Fowler says.
Step 3: Create a custom GPT.
A custom GPT uses retrieval-augmented generation (RAG) for knowledge retrieval between user queries and your data repository. There are specific tools that offer this (and I'll show you a few in a minute) but you may already have this functionality at your fingertips with your current AI tools.
Look at the features list of your AI tools and see if “custom GPTs” is an included feature. You can see this feature here with ChatGPT Plus:
Step 4: Analyze and optimize data.
Data analysis and optimization are key components of succeeding with an AI knowledge base.
“In terms of analysis and optimization, once you have the solution built, you have to start talking to it and interacting with it,” shared Isabella Bedoya. “See what responses it gives you — if you get inaccurate responses, then you know you need to either fix your knowledge base or your prompts.”
Use this step to identify knowledge gaps. Start prompting your knowledge base and see if it can understand:
- Abbreviations?
- Technical jargon?
- The complex concepts of your business?
Pro tip: Don't view data optimization as a “one and done” step in building your knowledge base. Analysis is an ongoing need, which leads us to step number five.
Step 5: Keep it up to date.
AI knowledge bases, just like traditional knowledge bases, need to be updated regularly to deliver accurate and relevant information.
“Let's say that a company is onboarding a new sales rep, but the data in their system is four years old,” said Chase Fowler. “Because they didn't optimize, they're training a new employee on software that they don't even use or products they don't sell anymore.”
Some important updates that would need to be reflected in your knowledge base are:
- Company changes.
- Software changes.
- Product updates.
- Policy updates.
Pro tip: Let customer feedback help inform your update process. Ask for user feedback at the end of their knowledge base experience. No one sees gaps as clearly as your customers.
Knowledge Base Software Options
Which AI knowledge base software is right for your needs? Let's take a look at the popular options.
Dante AI
Price: Get basic access for free, or upgrade to $19 per month.
Dante AI walks you through the onboarding process and even lets you choose customizations like tone for your bot. I named mine Botty and it digested hundreds of articles on my website and then started answering questions in a very similar tone to my writing (borderline creepy, Botty).
It couldn't be any easier to use and was trained on my entire website content in a few minutes.
I experimented with embedding the HTML directly into a popular blog post on my site to allow users to search for results:
If you pay $19 per month, you get access to the white-label version of Dante AI, and you can customize the way it looks on your site.
What I like: This is as easy as an AI tool could be; there's virtually no onboarding time, and you can have your site indexed into a knowledge base (for free) in only a few clicks.
Slite
Price: Try for free, or upgrade to $8 per month.
I liked the product introduction; I got to select my goals and company information, and then I was walked through an interactive onboarding process.
Slite creates a different type of knowledge from Dante AI. Dante AI made it very easy to upload bulk amounts of information (like scraping my entire website in two minutes), and Slite is more oriented towards bringing in Google Docs, Notion, etc., and creating very focused, searchable systems.
What I like: The search function that allows you to pull information from all of your company knowledge OR specific subcategories, like meetings. What an incredible time saver.
ChatGPT Plus
Price: $20 a month.
Have you ever noticed the “Explore” button on the left-hand side of ChatGPT? If you click on it, it takes you to a tab full of custom GPTs that other people have made and shared with the public:
It‘s this same customization power that enables you to create your own custom GPT knowledge base. Once you’re signed up for the paid plan, go to the ChatGPT editor and then configure your custom bot.
You‘ll fill the GPT with relevant information that you’d like to train your model on, and then you'll be able to test and optimize the results. You can see that process here:
Best for: People already paying for ChatGPT Plus who can stretch its functionality even further.
HubSpot Breeze
Price: Available in the Marketing Hub Professional for $890 per month.
Breeze is a massive tool that comes with more than 80 AI features. The Breeze Customer Agent uses your existing content to feed an AI agent. Breeze collects information and integrates it with other data sets inside your marketing hub to create a single source of truth.
What I like: Breeze Customer Agent is trained to detect when a real customer service agent is required and automatically reroutes queries to the appropriate member of your team.
Improve Your Knowledge Base With AI
I wasn't an early AI adopter, and I’ll be honest: I initially felt a lot of companies were being lazy when I saw them using it to interact with customers. All that changed when I saw just how much BETTER knowledge bases are when using AI.
From the outside, it all looks so seamless and well-integrated. Looking behind the curtain and seeing just how easy these systems are to implement, I don‘t know what anyone’s waiting for. Make your life and your customers' lives easier already by setting yourself up on one of these AI knowledge base platforms.
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