Your technical documentation has a new audience – AI, and it processes information very differently than humans do.
Every day, millions of developers, users, and customers engage with your documentation through AI tools. They ask ChatGPT to explain how your API works. They use GitHub Copilot to turn your examples into real code. They rely on AI-powered search to pull answers straight from your knowledge base. It’s all happening, driven by the quality of your docs.
What began as human-to-human communication has quietly evolved into a three-way conversation: human to documentation to AI to human.
This shift is a major opportunity and a hidden risk. Clear, well-structured docs help AI deliver accurate answers and strengthen your product. But vague or messy docs can quickly spread misinformation, with AI confidently passing along the wrong message.
The stakes are higher than ever. A single unclear paragraph can now mislead hundreds through AI. Yet documentation written with AI in mind often benefits humans, too: it’s clearer, more structured, and more useful.
The answer? Contextual Density Mapping: the art of giving AI just enough context to grasp concept relationships while keeping things readable and scannable for humans.
What is Contextual Density Mapping?
Contextual Density Mapping is the strategic practice of calibrating the amount of explanatory context to include in technical documentation to effectively serve both AI systems and human readers.
It’s about finding the right balance in the amount of information you provide. Enough context for AI to understand relationships and dependencies, but not so much that human readers get overwhelmed or lost in verbose explanations.
Think of it as creating layers of context that can be consumed at different levels: humans can skim for what they need, while AI systems can access the full contextual web to understand how concepts interconnect.
Humans and AI Process Text in Distinct Ways
Human readers typically:
- Scan for relevant sections
- Skip familiar concepts
- Fill in the gaps with prior knowledge
- Understand implicit relationships through experience
- Get frustrated with over-explanation of basics
- Get lost in too much content
AI systems typically:
- Process information sequentially or in chunks
- Don’t retain context between separate document sections
- Need explicit statements about relationships
- Can’t assume cultural or industry knowledge
- Benefit from redundant context that helps with accurate interpretation
Strategies for Optimal Contextual Density
The key to successful contextual density mapping is deploying specific techniques that address the different ways humans and AI systems process information.
Each technique provides a different approach to layering context. The goal is to build a toolkit of approaches you can mix and match depending on your content type, audience needs, and the complexity of the concepts you’re explaining.
The Context Sandwich
Structure information in layers that serve both audiences. Think of AI as somebody who needs to know everything about an API key. Not only what it is, but also why it’s needed and where to find it.
Poor example:
Use the API key in your requests.
Better example:
Authentication: Use the API key in your requests
An API key is an access token that a client provides when making API calls. It is a simple way to secure access and thus the most popular authentication method used with REST APIs.
The key can be sent in the query string or as a request header. You are automatically assigned an API Key once you create an account. Generate more keys and manage the existing ones through the Infobip API key management page.
“curl -L -g -X GET ‘https://{baseUrl}/sms/2/text/advanced’ / -H ‘Authorization: App 003026abc133714df1834b8638bb496e-8f4b3d9a-e931-478d-a994-28a725159ab9′” ’
The human gets the quick answer in the first sentence. AI gets the full context. This works well with your novice audience, which may also benefit from a longer explanation.
Explicit Relationship Mapping
Make connections between concepts crystal clear. Make sure you are specific about the chronology by either adding numbered steps or describing the sequence of steps explicitly. The goal here is to make sure your audience understands how individual steps, concepts, or components connect to form a coherent workflow.
Instead of simply listing steps:
- Buy a number
- Set up a webhook
- Configure your number
- Test the connection
Write:
Buy a number capable of receiving incoming SMS traffic. Set up a webhook. Add webhook details to the number you’ve purchased. Receive an SMS on your webhook.
And expand each step with clear explanations of why each action is necessary.
- Buy a number – To buy a number, you’ll need to log into your Infobip account.
The documentation then explains that you need to filter by SMS capability for the number to handle incoming SMS traffic.
2. Set up a webhook – For experimenting, you could use a simple free tool like Hook Relay, but in this tutorial, we’ll use ngrok so we can write some PHP locally to receive these webhooks.
This clearly explains the relationship between the webhook tool choice and the local development environment.
3. Configure your number with webhook details – Use the Forwarding dropdown to select the Forward to HTTP option. This will expand more options to configure. Select POST for a Method, and JSON as a Format. Under Forwarding provide the webhook’s URL.
The document explains that selecting “Forward to HTTP” expands additional configuration options, showing the cause-and-effect relationship.
4. Test the connection – “To test that all we have configured worked as expected, send an SMS from your handset to the number you have purchased. This should trigger an event, and you should see your message in a JSON format arriving at your webhook.”
This approach reduces cognitive load for human readers by eliminating the need to mentally connect steps or question their importance and order. By clearly explaining the sequence and purpose of each step, you allow readers to focus on understanding rather than interpretation, they can simply absorb the content. It may look longer, but it reads faster.
For AI systems, explicitly defined concepts and relationships reduce the risk of hallucination and improve their ability to troubleshoot by understanding the logical flow and connections between ideas.
Progressive Context Disclosure
Layer information from general to specific.
Start with a high-level overview
All API requests need to be authenticated through the Authorization header. The Infobip API offers the following authentication methods: HTTP Basic authentication, API keys, IBSSO tokens, OAuth 2.0. Select your preferred method to suit your current tech stack and security requirement level.
Then provide method-specific context. The document first provides key characteristics of authentication methods before diving into implementation detail.
API key header
An API key is an access token that a client provides when making API calls. It is a simple way to secure access and thus the most popular authentication method used with REST APIs.The key can be sent in the query string or as a request header. You are automatically assigned an API Key once you create an account. Generate more keys and manage the existing ones through the Infobip API key management(opens in a new tab) page.
Here are some key facts about this method:
- API keys can be generated by calling the dedicated API method
- Keys can be revoked at any time which is useful when separating the API access rights across multiple applications or use cases
- Infobip API keys have a predefined expiry date to eventually become invalid
Only after establishing the context does the document provide specific code examples.
Example – HTTP client (showing the raw implementation) Example – API client library (showing the simplified approach) .
Finally, the docs provide links to more detailed topics of advanced configuration, like API scopes that is relevant for implementation in practice but not necessary for theoretical understanding of the method.
This progressive structure allows readers to:
- Stop at Level 1 if they just need to understand authentication options
- Stop at Level 2 if they need to choose between methods
- Continue to Level 3 if they need implementation details
- Go to Level 4 if they need advanced configuration
Instead of hitting users with all technical details at once, progressive disclosure lets them consume information at their own pace. More importantly, they can stop at the level of detail they need.
For your human readers, starting with familiar concepts and building complexity helps information stick better than jumping straight into technical implementation.
Whereas AI systems can build more accurate mental models when they encounter information in logical order. AI systems often struggle with maintaining context across long documents. Progressive disclosure helps by front-loading the most important contextual information.
Contextual Anchoring
Anchor new concepts to previously established ones. Provide links and references to existing sections of your content.
Best practices for phone and email verification guide
Now that you understand what phone and email verification are, what the benefits are for your business, and how to implement each – it’s time to look at best practices when implementing these solutions.
Notice how it:
- Anchors new content (“best practices”) to previously explained concepts (“what phone and email verification are…”).
- Signals a logical progression from understanding to action.
- Uses clear linkage language (“Now that you understand… it’s time to look at…”).
This is a textbook use of contextual anchoring—establishing comprehension before building on it, guiding the reader step-by-step through related topics.
By connecting new concepts to what’s already been explained, it makes complex topics easier to grasp for the human audience. It reinforces the foundation and prepares the reader for the next layer of knowledge.
Your AI audience benefits from it as well because providing links between sections like that helps AI models interpret content relationships accurately. That in turn makes it easier for AI to map dependencies between concepts, improving how it summarizes, answers questions, or suggests related docs.
Contextual Sidebars
Use callouts that don’t interrupt the main flow.
Automatic agent assignment
You can set up the automatic agent assignment for calls. Go to Conversations → Settings → General setup → Queues.
For calls, availability is taken into consideration. An agent is considered available to take on new calls when they set their status to Available, and they have fewer conversations assigned in any status other than Closed. Workload and waiting times are calculated for every agent who is in the Available status.
Note : Calls are always auto-assigned by default, regardless of whether the queue’s auto-assignment is enabled.
This is the process when a new conversation is assigned to a queue.
Why it works as a Contextual Sidebar:
- Non-disruptive: It’s visually set apart as a sidebar/note so it doesn’t interrupt the main instructions.
- Contextually relevant: Placed right after guidance on configuring time-out, it adds a critical parameter without breaking the flow.
- Enhances understanding: Readers get essential information just when they need it – close to the configuration steps.
- Easy reference: It serves as a quick glance reminder without forcing the reader to dig deeper for this important piece of information.
With this method, you deliver key information just in time, keep your content clean, and improve scannability for readers who scan rather than read end-to-end.
For AI readers, sidebar metadata (like “Note,” “Warning,” or “Tip”) helps interpret tone and importance. AI knows a warning isn’t an instruction and often uses sidebars to answer questions thanks to their concise, focused content.
In short:
Writing for both humans and AI isn’t a compromise – it sharpens clarity, strengthens structure, and boosts usability.
When you effectively apply Contextual Density Mapping, your documentation becomes clearer, more reliable, and more useful for everyone, whether they’re reading it or parsing it.
