Sep 03, 2025·8 min
How to Build a Website That Explains AI to Non‑Experts
Step-by-step guide to plan, write, and design a website that explains AI capabilities clearly to non-experts, with examples, UX tips, and trust signals.
Step-by-step guide to plan, write, and design a website that explains AI capabilities clearly to non-experts, with examples, UX tips, and trust signals.
Before you write a single page, decide exactly who “non‑experts” are for your site. A “general audience” is rarely a real audience—and AI is easy to misunderstand when people arrive with different expectations.
Pick one primary group and (optionally) one secondary group. For example:
Give each group a quick profile: what they already know, what they’re worried about, and what decision they’re trying to make. This helps you choose the right level of detail—and the right examples.
Non‑experts typically scan for practical answers first. Start your content plan with the questions that show up in sales calls, support tickets, training sessions, and comments:
If you can’t answer these clearly, your site will feel like marketing—no matter how polished it looks.
Choose a small number of outcomes that matter. Common goals include:
Your goals should shape what you emphasize: clarity, reassurance, decision support, or hands‑on guidance.
Match metrics to goals so you can improve the site over time. Examples:
Set a review cadence (monthly or quarterly) and adjust content based on what people still misunderstand.
People understand AI faster when you group it into a few “jobs” it can do, rather than a long list of tools. Aim for 3–6 buckets that feel familiar and cover most of your content.
Choose categories your visitors can recognize from everyday work. Common options include:
Name each bucket with a simple noun (“Text,” “Images”) or a clear verb phrase (“Find answers in documents”). Avoid clever labels that require explanation.
Consistency reduces confusion. For each capability bucket, write four short parts:
This structure helps readers compare capabilities quickly and sets expectations without overwhelming detail.
Non-experts usually don’t need model names, benchmarks, parameter counts, or leaderboards. Replace them with user-facing guidance:
If you must mention technical terms, keep them optional (a brief note or tooltip) so the main page stays approachable.
A good AI explainer site feels predictable: visitors always know where they are, what to read next, and how deep they’re going. The goal isn’t to show everything at once—it’s to guide people from “I’m curious” to “I understand enough to decide.”
Keep your top navigation small and meaningful. A practical baseline sitemap looks like this:
This structure gives first-time visitors easy entry points, while also supporting repeat visits when someone needs a specific answer.
If you’re moving fast, it can help to prototype this structure as a working site rather than a static doc. For example, teams use Koder.ai (a vibe‑coding platform) to generate a React-based explainer site from a chat brief, then iterate with “planning mode,” snapshots, and rollback as content and navigation evolve.
Many non-experts don’t know what “capabilities” or “models” mean. Add a visible “Start here” path (from the home page and main menu) that leads through 3–5 short steps, such as:
Design each page in layers: a short overview first, then optional detail. For example, a capability page can begin with a one-paragraph summary, then expand into sections like “Typical inputs,” “Typical outputs,” “Best for,” and “Watch outs.” Visitors who want the basics can stop early without feeling lost.
Instead of long, overwhelming pages, connect related concepts. When someone reads about “hallucinations,” they should be prompted to check the glossary definition and a relevant FAQ entry. This turns your site into a guided learning experience rather than a pile of pages.
Plain language isn’t “dumbing down.” It’s removing avoidable friction so readers can understand what an AI system does, what it doesn’t do, and what to do next.
Aim for short sentences, active voice, and one idea per paragraph. This makes complex topics feel manageable without cutting important details.
If you feel accuracy slipping, add one extra sentence of context rather than switching to jargon. For example, instead of saying “the model generalizes,” say: “It learns patterns from past examples and uses those patterns to make new guesses.”
Most AI jargon has a simpler translation. Use the everyday version by default, and only introduce technical terms when they’re genuinely necessary.
Examples:
When you must use a technical term (because users will see it elsewhere), define it immediately in a single sentence. Then keep using that same wording.
Consistency reduces confusion more than extra explanations. Choose a single label for each key concept and stick to it everywhere.
For instance, decide whether you’ll say “AI system,” “AI model,” or “algorithm.” Pick one as the main term (e.g., “AI system”), and only mention the others once as alternate names readers may encounter.
Also keep verbs consistent: if you call the output a “suggestion,” don’t later call it an “answer” unless you’re intentionally changing the expectation.
Start each page with a short “what you’ll get here” summary in 3–5 bullets. This helps non-experts orient quickly and reduces misinterpretation.
A good summary typically includes:
This approach keeps the main text readable, while still preserving the precision people need to use AI safely and confidently.
People understand AI faster when you show it as a simple system: what goes in, what happens, what comes out, and what the person should do next. A small diagram can prevent long explanations and reduce “magic box” thinking.
Be explicit about what a visitor must provide. Common input types include:
A helpful pattern is: “If you give it X, it can do Y; if you don’t, it will guess.”
Name the output in plain terms, and show what it looks like:
Also note what the output is not: a guarantee, a final decision, or a perfect source of truth.
A simple diagram can fit on one screen:
Input Processing Output
(prompt / files / data) (AI finds patterns + predicts) (draft / label / suggestion)
│ │ │
└─────────────────────────┴───────────────────────────┘
Review
(human checks, edits, verifies)
Keep the “Processing” box high-level. You don’t need internal model details; the goal is clarity, not engineering.
Right next to the diagram, include a short “before you use this” note:
This turns the diagram into a practical workflow visitors can follow immediately.
Examples are where AI stops feeling abstract. Aim for 5–10 real‑world examples per capability (one page or panel per capability), written as short, relatable scenarios people recognize from daily work.
Keep each example consistent so readers can scan:
Use these as models, then create similar sets for summarizing, brainstorming, data help, customer support drafts, and so on.
Before: “I need this by end of day. If you can’t do it, tell me now.”
After (AI‑assisted): “Could you share an update by 5pm today? If that timing won’t work, let me know and we’ll adjust.”
What you should check: tone matches your relationship; no promises added; remove sensitive details.
Before: “Talked about launch. Some risks. Sam mentioned vendors.”
After (AI‑assisted): “Actions: (1) Sam to confirm vendor lead times by Wed. (2) Priya to draft launch checklist by Fri. Risks: vendor delays; unclear approval owner.”
What you should check: names/owners correct; dates accurate; missing decisions filled in by you, not guessed.
Before: “Looking for a rockstar who can handle anything under pressure.”
After (AI‑assisted): “Seeking a coordinator who can manage deadlines, communicate clearly, and prioritize tasks across teams.”
What you should check: biased language removed; requirements are real; accessibility and inclusivity.
Before: “Not our fault. You used it wrong.”
After (AI‑assisted): “I’m sorry this was frustrating. Let’s figure out what happened—can you share the steps you took and the error message?”
What you should check: aligns with policy; no admissions of fault; privacy (don’t request unnecessary data).
Before: “Your request is pending due to insufficient documentation.”
After (AI‑assisted): “We can’t finish your request yet because we’re missing a document. Please send: proof of address (dated within 90 days).”
What you should check: accuracy of requirements; clarity for non‑native readers; avoid collecting extra personal info.
Downloadable prompts can be helpful, but only publish them if you can keep them current. If you do, label them with a last updated date, note what model/tool they were tested with, and provide a simple way to report when they stop working.
People don’t need a math lesson to understand uncertainty—they just need you to say it plainly and consistently. A helpful framing is: an AI system predicts likely outputs based on patterns in data; it doesn’t “know” facts the way a person does. That one idea prevents a lot of confusion, especially when the model sounds confident.
Be specific about how AI can fail, using everyday language:
A good website doesn’t hide these issues in fine print. Put them next to the feature they affect (for example, mention hallucinations on any page about “summarizing” or “answering questions”).
Use wording like: “The system chooses the most likely next words based on patterns it learned.” Then add what that implies: “That means it can be confidently wrong.” If you show confidence scores or “may be inaccurate” labels, explain what users should do next (double-check, request sources, compare with trusted references).
If your site promotes AI for decisions, include a clear warning block for medical, legal, and financial uses: AI output is not professional advice, may omit critical details, and should be reviewed by a qualified expert. Avoid vague cautions—name the risks (misdiagnosis, compliance issues, incorrect tax guidance).
People don’t need to understand every technical detail to feel confident using your AI. They do need clear, specific answers to “What happens to my data?” and “What keeps this safe?” Make trust a first-class part of your site—not a footnote.
Create a dedicated page that explains what you collect, what you don’t collect, and why. Keep it readable and concrete, with examples of common inputs.
Include items like:
Non-experts often assume AI output is “verified.” Be careful with wording. Describe your safeguards at a high level—without implying perfect protection.
Examples of safety notes to include:
Give users a short “Use this well” section that explains appropriate scenarios and red flags. Pair it with a clear escalation path:
Trust grows when people can see who is behind the product and how it’s maintained. Add:
When transparency is consistent and specific, your AI explanations feel less like marketing—and more like guidance users can rely on.
A glossary and FAQ act like “training wheels” for readers who don’t know the terminology yet. They also help experts stay aligned on definitions, so your site doesn’t accidentally use the same word to mean different things.
Keep entries short, concrete, and written for someone who’s never taken a computer science class. Start with the terms readers bump into most often:
Add a small line under each entry: “You might also hear…” and list common synonyms or nearby terms to prevent confusion, for example:
On capability pages, add subtle tooltips for glossary terms the first time they appear. Keep them to one sentence and avoid jargon inside the definition. Tooltips work best when they:
Your FAQ should answer what people are already wondering (or worrying) about. Good questions to include:
When glossary + FAQ are easy to find and consistent, readers spend less time decoding terms—and more time learning what the AI can actually do.
A site that explains AI well should feel effortless to read. When people are learning unfamiliar concepts, the design should reduce strain, not add to it.
Start with typography and spacing choices that support comprehension:
Break dense ideas into short paragraphs, and use clear headings to signal what each part is for. If you need to introduce a term, consider a brief callout box that defines it in one sentence before continuing.
Non-experts often skim first, then decide what to read.
Use consistent page patterns: a clear headline, a one-paragraph “what you’ll learn,” and structured sections with descriptive subheadings. Make navigation predictable (top menu + breadcrumbs or a visible “Back to overview”), and avoid hiding key pages behind clever labels.
Callouts can help, but keep them purposeful—use them for “Key takeaway,” “Common misconception,” or “Try this prompt,” not for repeating the same point.
Accessibility improvements benefit everyone, including people on mobile and in noisy environments.
Ensure:
AI explanations often rely on flows and comparisons—these can break on small screens.
Use stacked cards for step-by-step pipelines, accordions for definitions and FAQs, and side-by-side comparisons that collapse into vertical “Before” then “After.” Keep tap targets large, and avoid interactions that require precision (like tiny hover-only tooltips).
A good AI explainer doesn’t end with “now you know.” It helps people decide what to do next—without pushing everyone toward the same action.
Offer a small set of clear calls to action (CTAs), each tied to a different goal:
Keep the wording concrete: what they’ll get, how long it takes, and what they need to provide.
If you’re offering a hands-on path, consider a “Build a sample app” CTA for readers who learn by doing. Platforms like Koder.ai can turn a short chat brief into a working web experience (React front end with a Go/PostgreSQL backend), which is useful for quickly validating your IA, demos, and content flows—then exporting source code when you’re ready to operationalize it.
Don’t force expert users through beginner content—or beginners into technical rabbit holes. Use lightweight “paths,” such as:
This can be as simple as two buttons near the top of key pages (“I’m learning” vs “I’m evaluating”).
If you include a form, say what you need (example files, industry, goal, constraints) and what happens next. If you can, add:
AI information ages quickly. Assign an owner, set a review cadence (monthly or quarterly), and add simple versioning notes (e.g., “Last reviewed: Month YYYY” and “What changed”) so readers can trust the content stays current.
If your explainer is tied to an interactive demo or a tool experience, treat updates the same way you treat software releases: track changes, keep a clear rollback option, and document what changed. (This is also where tooling features such as snapshots and rollback—available in platforms like Koder.ai—can reduce risk when you’re iterating quickly.)
Start by picking one primary non-expert group (and optionally a secondary one). Write a quick profile for each:
This keeps your explanations at the right level and prevents “general audience” vagueness.
Pull questions from real sources: sales calls, support tickets, onboarding sessions, and comments. Prioritize questions that affect trust and decisions, such as:
If you can’t answer these clearly, the site will read like marketing.
Pick 1–3 goals tied to outcomes you actually care about. Common examples:
Then align every major page to at least one goal so the site stays focused.
Match metrics to goals and review them on a schedule (monthly or quarterly). Useful metrics include:
Use the results to update content where people still get stuck.
Group features into 3–6 recognizable “jobs” (e.g., Text, Images, Audio, Search & Q&A, Spreadsheets). This helps visitors understand faster than a long tool list.
Keep bucket names simple and literal (avoid clever labels that need explaining).
Use the same mini-template everywhere:
Consistency makes it easy to compare capabilities without deep reading.
Usually skip model names, benchmarks, parameter counts, and leaderboards. Replace them with user-facing guidance like:
If you must include technical terms, keep them optional (tooltips or short notes).
Keep top navigation small and predictable. A practical baseline is:
Add a prominent path that guides beginners through a short sequence: what it is, what it’s good at, where it fails, relatable examples, and next steps.
Write in short sentences, active voice, and one idea per paragraph. Replace jargon with everyday equivalents (and define unavoidable terms immediately).
Also pick one consistent term per concept (e.g., always “AI system,” not switching between “model,” “engine,” and “algorithm”). Consistency prevents confusion more than extra length.
Put limitations next to the features they affect (not buried in fine print). Explain uncertainty plainly:
Add clear high-stakes warnings for medical, legal, and financial use, and tell people what to do next: review, edit, verify, and escalate when needed.
| Best for |
|---|
| Not for |
|---|
| Drafting first versions of emails, summaries, and outlines | Diagnosing medical conditions or changing treatment plans |
| Brainstorming options and questions to ask | Legal interpretations, contract approval, or compliance sign-off |
| Explaining concepts at a beginner level | Making final financial decisions or investment recommendations |
| Organizing notes and generating checklists | Any task requiring guaranteed accuracy without verification |