Adding Knowledge to Prompts

Lesson 10: Making Knowledge Work in Your AI Content Operations

Imagine you're developing training materials for a new employee onboarding program. Your company has just updated its project management software, internal communication protocols, and security procedures.

While you could simply ask AI to help generate training guides, relying on its default knowledge would be risky. The AI might draw on generic onboarding practices or outdated business procedures, creating materials that don't reflect your company's specific workflows and culture.

Consider the complexity of what new employees need to learn: how to use multiple software tools, when to use different communication channels, and which security practices apply to different situations. Your training materials need to be accurate, accessible, and aligned with your company's specific practices. More importantly, they need to build on each other in ways that help employees develop confidence and competence progressively.

This scenario illustrates a fundamental challenge in AI-assisted content development: how to effectively provide and structure knowledge in our prompts. While AI models are trained on vast amounts of internet data, this default knowledge comes from unknown sources and may be outdated, biased, or simply incorrect.

For content developers and training teams, relying solely on this default knowledge isn't just risky – it can lead to confusion, inefficiency, and potential security issues.

Think about how you develop training materials traditionally.

You consult company policies, interview experienced employees, review existing documentation, and consider what has worked well in previous training sessions. You might reference similar programs or compare different teaching approaches. You consider your audience's background and common challenges.

Working effectively with AI requires bringing all these knowledge sources together in structured ways that help the AI understand and use them effectively.

How AI Processes Knowledge

To use knowledge effectively in our content development workflows, we first need to understand how AI processes the information we provide. Think of AI as a new team member who has read vast amounts of content from the internet but needs guidance about your specific organization and requirements.

Modern AI language models convert text into mathematical representations called vectors - essentially turning words and concepts into numbers that they can process.

These vectors don't just represent individual words; they capture relationships between concepts. For instance, in employee training, the AI understands that "onboarding" relates to concepts like "orientation," "training modules," and "new hire paperwork" because these ideas frequently appear together in its training data.

When you provide new knowledge in a prompt, the AI processes it by connecting your specific information with its general understanding.

Let's say you're creating onboarding materials about your company's unique approach to project management. The AI already understands basic project management concepts from its training, but it needs your specific knowledge about how your company handles project timelines, team collaboration, and resource allocation.

This process is similar to how an experienced trainer might adapt general business practices to fit your company's specific culture. Just as that trainer draws connections between standard practices and your unique implementations, the AI attempts to bridge its general knowledge with your specific requirements.

For longer documents or comprehensive resources like employee handbooks and training manuals, AI systems use a technique called Retrieval Augmented Generation (RAG).

Imagine having an extremely thorough research assistant who can instantly search through all your company documentation, find relevant information, and incorporate it into new training materials. When you upload documents like company policies, existing training guides, or process documentation, the system breaks them down into smaller, manageable pieces and converts each into vectors.

For example, if you're developing a module on communication protocols, RAG could help you:

• Find relevant sections from your company's communication policy

• Reference examples of effective internal communications

• Pull specific guidelines from your style guide

• Identify common communication challenges from HR documentation

However, just like you wouldn't dump a stack of policy documents on a new hire's desk without guidance, you need to help the AI understand which parts of these materials are most relevant to your current task. Simply uploading documents isn't enough - you need to provide context about how this knowledge should be used in your current content development project.

Understanding this process helps us develop more effective strategies for structuring information in our prompts.

In the next section, we'll explore practical approaches to organizing knowledge that help AI create more accurate and useful content for your specific needs.

Making Knowledge Work Effectively

Understanding how AI processes knowledge leads us to an important question: How do we provide information in ways that help AI create truly useful content?

The answer lies in thoughtful structure and clear organization, much like how we organize training materials to build understanding progressively.

Remember our onboarding program example. When an experienced trainer develops new modules, they don't simply present information randomly. They organize content carefully, establish key concepts first, and build on that foundation. We need to take a similar approach when providing knowledge to AI.

One powerful method borrows from a technology called XML, which uses tags to label different types of information. While you don't need to understand XML technically, this concept of "tagging" knowledge helps organize information in ways that AI can use more effectively. Think of it like creating clear sections in a training manual, each serving a specific purpose.

For instance, instead of providing a jumbled list of information about your communication protocols, you might structure it like this:

<communication_policy> Our company uses Slack for immediate team communication, email for formal communications and external contacts, and Microsoft Teams for video meetings and file sharing. All sensitive information must be shared through our secure internal portal. </communication_policy>

<common_challenges> New employees often struggle with knowing which communication channel to use when. They frequently default to email when real-time collaboration would be more effective, or use public channels for sensitive discussions that should be private. </common_challenges>

<success_metrics> Employees should be able to:

- Choose appropriate communication channels for different situations
- Follow security protocols for sensitive information
- Effectively collaborate using our digital tools
- Maintain professional communication standards across all platforms </success_metrics>

This structured approach helps the AI understand not just what each piece of information is, but how it should be used in creating training materials. When you later ask the AI to help develop specific modules or exercises, it can draw on this clearly organized knowledge to create more targeted and effective content.

Let's look at how this plays out in a real content development scenario. Imagine you're creating a series of microlearning modules about communication tools. You might structure your prompt like this:

<learning_context> These modules will be delivered as 5-minute video scripts, focusing on one communication tool per module. Learners will watch these during their first week, before they start actively using the tools. </learning_context>

<tool_specifics> Microsoft Teams features we use most:

- Channel-based team discussions
- Video meetings with screen sharing
- Document collaboration
- Integration with other Microsoft tools </tool_specifics>

<audience_background> Our new hires typically have experience with basic email and chat tools, but many haven't used Teams in a professional setting. They're often anxious about making mistakes in company-wide channels. </audience_background>

By providing knowledge in this structured way, you help the AI understand:

• The format and constraints of the content it needs to create

• The specific features that need to be covered

• The audience's starting point and concerns

This allows the AI to generate content that's not just accurate, but truly useful for your specific situation and audience.

This structured approach to knowledge organization in prompts connects directly back to our earlier discussions about taxonomies.

Taxonomies help us organize our prompt libraries and guide how we structure knowledge within individual prompts. Think of it as creating a miniature knowledge taxonomy for each content development task.

Remember how we discussed organizing prompts into categories that reflect natural workflows and relationships? The same principles apply when organizing knowledge for specific content tasks.

Your knowledge structure should reflect both the natural relationships between different pieces of information and the practical ways that information will be used in content creation.

For instance, in our training materials example, we might organize our knowledge following similar patterns to our prompt taxonomy:

Core Knowledge - This includes fundamental information that shapes everything else: company policies, essential procedures, and key objectives. Like the main departments in our prompt taxonomy, this forms the foundation of our content.

Contextual Knowledge - This provides important background and situational information, much like how our taxonomy creates connections between related prompts. It helps the AI understand how to apply the core knowledge effectively.

Implementation Knowledge - This includes specific examples, common scenarios, and practical applications. Similar to how our taxonomy helps us find the right prompts for specific tasks, this knowledge helps the AI create content that bridges theory and practice.

By aligning our knowledge organization with our broader taxonomical approach, we create consistent patterns that both humans and AI can follow more easily. This consistency helps ensure that whether we're organizing our prompt library or structuring knowledge within individual prompts, we're working within a coherent, systematic framework.

As you develop your own approach to knowledge structuring, consider how it might align with your existing content organization systems.

How can your prompt taxonomy inform the way you structure knowledge? How might your knowledge organization patterns suggest new ways to organize your prompt library?

Direct Knowledge vs. Reference Materials: Finding the Right Balance

When developing content at scale, you'll often work with both small, focused pieces of information and larger bodies of documentation. Understanding when and how to use each type of knowledge is crucial for effective content development.

Think of it like planning a training program. Sometimes you need to provide specific instructions directly to a trainer - key points that must be covered, particular examples to use, or specific language choices.

Other times, you give them access to comprehensive training manuals and let them draw on that broader resource as needed. Working with AI requires similar judgment about when to provide knowledge directly and when to use reference materials.

Direct Knowledge Inclusion

Direct knowledge works best when you need precise control over how information is used. You're essentially telling the AI, "Use exactly this information in exactly this way." This approach is particularly valuable when accuracy and consistency are crucial.

For example, imagine you're creating a series of safety procedure guides. You might provide direct knowledge like this:

<safety_procedures> Emergency evacuation requires:

1. Immediate cessation of all activities
2. Exit through marked emergency routes only
3. Assembly at designated meeting points
4. Attendance check by department supervisors </safety_procedures>

<critical_context> Recent safety audit identified confusion about assembly points. Multiple employees attempted to use unauthorized shortcuts during our last drill. New materials must emphasize following marked routes only. </critical_context>

<compliance_requirements> All safety documentation must include:

- Clear step-by-step instructions
- Visual guides or diagrams
- Emergency contact information
- References to relevant safety regulations </compliance_requirements>

By providing this knowledge directly in your prompt, you ensure these crucial details are immediately available and prominently featured in the generated content.

Working with Reference Materials

For comprehensive resources like company handbooks, technical specifications, or extensive training materials, file references often work better. Instead of copying large amounts of text into your prompts, you can upload these documents and guide the AI to relevant sections.

However, simply uploading documents isn't enough. You need to provide context about how these materials should be used. Consider this approach:

<reference_materials> I've uploaded our complete employee handbook and safety manual. Focus specifically on:

- Chapter 3: Emergency Procedures
- Appendix B: Assembly Point Locations
- Section 7.2: Department Supervisor Responsibilities </reference_materials>

<content_goals> Create step-by-step guides that:

- Simplify complex procedures without omitting crucial steps
- Incorporate relevant visuals from the reference materials
- Maintain consistency with existing documentation </content_goals>

This combination of focused guidance and comprehensive references helps the AI create content that's both accurate and well-integrated with your existing materials.

Finding the Right Balance

The key to effective knowledge management is understanding when to use each approach. Consider using direct knowledge when:

• Specific wording or details must be precisely maintained

• You're working with critical information that can't risk misinterpretation

• You need to ensure certain elements are prominently featured

• You're establishing new standards or procedures

Use reference materials when:

• Working with comprehensive documentation that provides important context

• Creating content that needs to align with existing materials

• Dealing with complex topics that require drawing from multiple sources

• Maintaining consistency across a large body of content

Often, the most effective approach combines both methods, using direct knowledge to guide the AI's focus while keeping broader reference materials available for context and alignment. This hybrid approach helps ensure your content is both precisely accurate and consistently integrated with your larger documentation ecosystem.

Think of it as building a bridge between your immediate content needs and your broader knowledge base. Direct knowledge forms the essential structure, while reference materials provide the supporting framework that connects it to your larger content landscape.

➡️ Want to dive deeper into implementing these approaches in your content workflows? Paid subscribers get access to specific frameworks and examples, as well as tools like worksheets to help you get started on developing your prompt operations.

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