Preparing Product Content for AI: Unified Knowledge and Governance in Technical Documentation
Share
Whether you’re actively exploring AI-powered tools or not, preparing your content now is crucial for future success. However, according to Salesforce, 75% of companies struggle to scale AI, partly due to disconnected systems and data. The answer to this challenge is a unified knowledge framework that provides a structured approach to creating, managing, and storing technical content.
We’ll explore why this framework, encompassing consistent language, taxonomies, formats, and a solid content governance strategy, is essential for delivering a seamless customer experience. More importantly, we’ll discuss how unified knowledge is the bedrock for effective AI, particularly with generative AI.
From ensuring accuracy in AI-generated responses to protecting business-critical knowledge, we’ll cover the key strategies and technologies, including component content management systems (CCMS) and robust content governance, that will make your content AI-ready and position your business for the future of customer support and growth.
Ready to dive in?
What Do We Mean By Unified Knowledge?
Think about all the technical content you create to support customers: technical documentation, in-help content, knowledge base articles, support tickets, API documentation, training content, and even community discussions (such as a Slack community for customers). Typically, you would create all these content types separately, often by different teams, and publish them to different channels.
Any system or network drive can “centralize” all this knowledge. But just because your information is stored in a central location doesn’t mean it’s unified.
Unified knowledge means you have implemented a framework for creating, managing, and storing all your technical knowledge information. This framework includes a shared structure and language, taxonomies and metadata, consistent terminology, and content formats. It outlines the people, processes, and technology you need to ensure your knowledge is accurate, consistent, and always up to date, regardless of the channel where it’s published.
A unified knowledge strategy is important because it helps companies deliver a consistent customer experience. No matter where the customer gets their technical information, it is always the same and always correct.
A good example is a company that sells several different products. Each product team writes the technical documentation for their product. They each have their own templates, guidelines, and processes for creating and managing the documentation for their product. And it doesn’t match the other teams. What happens if a customer buys multiple products from the company (which is what companies want their customers to do)? What they are used to getting from one product may not match what they get from another product. In this case, it can confuse them and slow them down in learning to use the product. Plus, information that should be the same across products isn’t because each team manages that information differently.
Inconsistencies in the actual content and how it is presented cause confusion and frustration because they make it harder for the customer to use the new product. Frustrated customers don’t stick around long; they leave for other companies that ensure their product information is consistent and easy to understand.
A unified knowledge strategy involves making it easier to understand and use your product (or service).
Preparing and Delivering Content for Successful AI Deployment
Learn more in this deep-dive webinar.
Why is Unified Knowledge Critical for AI?
Unified knowledge is important for companies that want to use artificial intelligence (AI) to deliver newer, modern customer experiences, such as conversational interfaces that use generative AI. Gartner predicts that by 2026, 80% of companies will have used generative AI APIs or deployed generative AI-enabled applications into production.
Generative AI has become a top priority for the C-suite and has sparked tremendous innovation in new tools beyond foundation models,” said Arun Chandrasekaran, Distinguished VP Analyst at Gartner. “Demand is increasing for generative AI in many industries, such as healthcare, life sciences, legal, financial services and the public sector.
Generative AI is a branch of AI that generates text-based content in response to a prompt (a request for information). Its response is based on the knowledge it has built from ingesting and analyzing large amounts of content. For an in-depth understanding of how generative AI works, check out this article.
Product content is a natural use case to get started with. Why? Behind every product is a ton of documentation on how that product is built, how it works, and how best to use it. Plus, think about all the knowledge base content created from support tickets, all the customer questions in your customer community, and the conversations between your development and engineer teams in your team slack groups.
A company can use generative AI to build a large language model (LLM) that includes product information and then provide self-service experiences that help customers get answers and learn to use the products better. Because of the way generative AI works, including how you label and tag your content, the content and language used in responses can be adapted to the customer’s level of experience.
But generative AI is only as good as the information behind it. Your job is to ensure that the technical product information is accurate and easy for the AI to digest and understand.
A unified knowledge framework does precisely that. It helps the AI better understand the context and relationships of the information it ingests, improving its ability to provide accurate, holistic answers to even the most complex query (prompt). It also reduces the likelihood of the AI hallucinating and making up incorrect information.
Val Swisher points out that to train AI well, the training content it ingests must be cleaned up. If you want the AI to produce useful results, you need to remove redundant, inaccurate, and inconsistent content.
All the things we do to make content better for humans – componentize it, structure it, clean up the grammar, tag it with metadata – make content better for machines as well.
When it comes to product-specific documentation, AI-generated answers need to be comprehensive (it must include information from all sources), accurate, context-aware (product version, role, etc.), and regularly updated.
If you don’t provide your AI with unified knowledge? The AI will have to ingest content from different business areas managed by different teams using different systems, and it will have challenges making connections across those systems. In the end, you risk the AI returning fragmented or inconsistent responses and sometimes even the wrong ones.
The Right Technology is Key to a Unified Knowledge Framework
Having the right technology is one of the challenges to creating unified knowledge. Although companies understand the need for technical content, they don’t truly see its value, so they don’t always invest in the right technology to create and manage content.
From a technology perspective, the best way to unify your technical content is to use a component content management system (CCMS). By storing all your technical knowledge in a CCMS, you provide a single source of truth for that technical content.
We’re not just talking about putting the information in a central location but also providing the unified knowledge framework you need to ensure that all that information is created and managed consistently.
A CCMS provides a structured content model and shared taxonomy. It also supports content reuse, so you can reuse content across your publications, eliminating duplicate content that is hard to track and keep in sync. CCMSs like Paligo also include collaboration and communication features that facilitate knowledge exchange among your technical writers, managers, and subject matter experts (not just for one product line but also across product lines).
When You Use AI, Content Governance Can Protect Business-Critical Knowledge
You know you want to take advantage of AI to deliver great customer experiences, but you’re worried that you may be exposing business-critical knowledge to people who shouldn’t have access to it, including companies that use the knowledge to train their large language models.
The good news is that you can leverage AI and protect your knowledge, too. It just requires a strong content governance strategy. Plus, content governance is a critical element of any unified knowledge framework.
What do we mean by content governance in product content?
Content governance for technical documentation refers to the systems, processes, and standards a company implements to ensure the quality, consistency, accuracy, accessibility, and findability of its technical content. It covers the entire content lifecycle, from content creation and management to publication and eventual archiving.
Some of the elements you will find in a content governance strategy include:
- Content models: Content strategists spend a lot of time understanding your content and defining its structure and organization to ensure customers can easily find the information they need. A content model includes a structured content model, metadata and taxonomy, information architecture, and templates for publication types and components.
- Roles and responsibilities: Many different people are involved in creating and managing technical documentation. It’s important to define the roles of everyone involved, mapping individuals to a role with clearly defined responsibilities and permissions. This mapping ensures that individuals can only perform their assigned tasks and have proper access to the documentation.
- Standards and style guides: Unified knowledge has a consistent writing style and tone. It uses the same terminology, follows similar formats, and consistently uses the same types of visual elements (e.g., screenshots and diagrams).
- Workflows and processes: All documentation goes through a review and approval process. This is mapped out in a workflow, where specific roles are identified for each stage of the process to perform specific actions. However, workflows are not only for reviews; they can be created to support additional stages of content development and ensure that content is created and managed properly. Some additional workflows include a collaboration workflow, an archiving workflow, and a content update workflow.
- Quality assurance: The role of quality assurance is to ensure documentation is tested for accuracy, clarity, and completeness, as well as to confirm that it adheres to the defined style guide.
- Localization: If technical documentation needs to be available in multiple languages, a translation and localization process outlines how the process works and identifies any specific localization requirements (e.g., how a product is named across locales).
- Accessibility: Accessibility is often an afterthought when it should be an important component of content governance, outlining the guidelines writers and editors must follow to ensure their content is accessible to everyone.
- Content maintenance: Once technical content is published, it still needs to be regularly reviewed to keep it current. A content maintenance plan defines when and how content is reviewed, including when to archive it.
- Metrics and reporting: Tracking key metrics related to content performance is important to help identify areas of improvement, where common issues tend to surface, and to measure the impact of your content governance efforts.
- Technology: Your content governance strategy must identify the technology you will use to manage your technical content. A CCMS is one technology. Others can include a digital asset management system (DAM) and content delivery platforms like Zoomin and FluidTopics.
It’s also important to clearly define who is responsible for different aspects of content governance, including setting standards, enforcing policies, and managing the overall process. This might include creating a service-oriented content management model like content-as-a-service and should include roles like a Content Strategist, Content Operations Manager, or a designated Content Governance Committee.
Content governance specific to AI
The content governance components defined so far apply to creating and managing technical knowledge that every company should have in place. However, there are additional governance requirements for content that will be ingested by AI and for content that AI generates.
Select the right documents to use in the AI training set
First, not all documentation should be used to train the AI. Create a rigorous process for selecting, cleaning, and verifying the technical documentation you will use in your AI training dataset.
Focus on up-to-date documentation. You don’t want to train the AI on outdated technical information. Doing so would result in inaccurate responses from the AI. Always work with the most recent version of your published documentation and identify when that documentation is updated so you can train the AI on the new versions when they are ready.
You must also carefully control what documents are fed into the LLM to avoid unintentionally exposing confidential information or violating intellectual property rights.
Train the LLM on your content standards
LLMs do not return the actual text in your documentation (see how LLMs work here). They write brand new text based on the content they are trained on. So, it’s vital that you train the LLMs on your company’s specific writing standards and style guides so that they return text that follows those guidelines.
Keep humans in the loop
Human oversight and control are essential when selecting training sets for AI. In addition to having accurate and up-to-date documentation in the training set, you also need to ensure your training set will not contribute to any bias in language, tone, or examples. This is another reason the standards and style guides are important to follow.
Part of your LLM training process should include writing clear and specific prompts that guide the LLM in generating desired documentation outputs. This includes training the LLM on domain-specific content (e.g., medical equipment, manufacturing processes), validating its output for technical accuracy and completeness, and adhering to style guides. Fine-tuning the LLM on specialized technical documentation datasets improves its performance and accuracy within your specific domain.
If you are using generative AI to help you write some of your technical documentation, keep in mind that LLMs should be treated as powerful writing assistants, not replacements for human writers and editors. Implement mandatory review and editing processes to ensure accuracy, clarity, and adherence to style guides.
Be transparent
When you use customer-facing applications that leverage generative AI, be transparent about how it is trained and used. Explain any potential limitations and how you have attempted to ensure the content the AI returns is accurate and complete. Your customers should understand your process for training the LLMs and the responses returned and should have a method to provide feedback when errors or inconsistencies are found.
A Step-by-Step Guide
Develop a comprehensive understanding of product knowledge documentation.
Having Your Content AI-Ready Makes Good Business Sense
According to Accenture global research, 84% of C-suite executives believe they must leverage artificial intelligence (AI) to achieve their growth objectives. A key part of growth is retaining and growing the customers you have. And a key part of retaining and growing customers is ensuring they have the information they need to use your products successfully, when they want it and how they want it.
Generative AI is a natural fit for customer-facing applications that provide product support. You can use it in self-service tools that allow customers to ask questions and get answers. It can support customer support representatives as they work to resolve customer problems. It can also help technical writers create technical documentation faster while following defined standards. The key is that it reduces the time needed for customers to get the answers to their questions and allows support teams to focus their attention on more critical customer issues.
Generative AI can also support sales teams as they build relationships with potential customers. Sales reps often get asked product questions they don’t immediately know the answer to. When you train your LLM on technical documentation, including any “tribal knowledge” located within teams or slack groups and customer communities, your sales reps can quickly find the correct answers and provide them to the prospective customer.
However you chose to leverage generative AI, having all your technical knowledge AI-ready is critical to ensuring the responses it generates are accurate, complete, and answer the question/prompt.
The Wrap
Too often, we think we need to do new things to support new technologies like generative AI. But the truth is that when it comes to creating the right technical knowledge to support customers, we should already be implementing processes that will support these new technologies.
A unified knowledge framework and content governance strategy are things you should do to support customers, even if you don’t leverage tools that use AI. When you are ready to implement AI-enabled tools, your content will be AI-ready.
You can learn more about preparing your content for AI by watching the on-demand webinar Preparing and Delivering Content for Successful AI Deployment.
Get started with Paligo
Paligo is built to meet the most demanding requirements, with plans made for any company from the growing SMB to the large Enterprise.
Share
Author
Barb Mosher Zinck
Barb Mosher Zinck is a senior content marketer and marketing technology analyst. She works with a range of clients in the tech market and actively tracks and writes about digital marketing, customer experience and enterprise content management. Barb understands the value of technology and works hard to inform and encourage greater understanding of its role in the enterprise