My 30-second specificity test
I open the landing page, give it thirty seconds, and then try to answer four questions without filling in the blanks myself:
- Who is the tool for? Not "teams" or "creators" in general, but a recognizable role or workflow.
- What input does it need? A repo, a prompt, meeting audio, screenshots, invoices, product analytics, support tickets, or something else?
- What output does it create? A pull request, a structured report, a draft, a transcript, a dashboard, an image, a spreadsheet, or an API response?
- What decision does it help you make? Ship faster, reduce review time, choose a model, triage leads, summarize calls, or replace a manual step?
If the page cannot answer these four questions, do not treat the claim as a product promise. Treat it as a hypothesis that still needs proof.
Eight receipts I look for
| Receipt | What to look for | Why it matters |
|---|---|---|
| Input receipt | Clear examples of what the user uploads, connects, or writes. | You cannot evaluate workflow fit without knowing the required input. |
| Output receipt | Screenshots or samples of the actual result, not only abstract benefit language. | The output reveals whether the product saves work or creates review debt. |
| Boundary receipt | What the product does not do, what it requires from you, and when it fails. | Good tools make tradeoffs visible. Bad tools hide them behind broad claims. |
| Workflow receipt | A short sequence: connect, configure, review, approve, export, deploy. | AI products usually fail at handoff points, not at demo generation. |
| Pricing receipt | A visible pricing page or a clear reason pricing is custom. | Hidden pricing increases the cost of comparison and often hides usage limits. |
| Data receipt | What data is stored, used for training, retained, or shared. | Input data is often the real risk, especially for files, code, customers, or meetings. |
| Control receipt | Approval steps, undo, logs, export, version history, and permission controls. | The more an AI tool can do, the more important reversibility becomes. |
| Comparison receipt | When to use it instead of a spreadsheet, an IDE plugin, a script, or a cheaper model. | A serious product knows what it competes with. |
Weak claim vs useful claim
Vague pages often sound polished because they sell an emotion rather than a workflow. The useful version names the job, the artifact, and the limit.
| Weak claim | Better claim |
|---|---|
| "Supercharge your team with AI." | "Turn support tickets into draft replies with source links, confidence labels, and manager approval before sending." |
| "Build apps ten times faster." | "Generate a first React form from a schema, then open a pull request for human review." |
| "AI insights for your business." | "Summarize weekly Stripe, GA4, and support data into a retention-risk memo." |
| "The future of creative AI." | "Create four product mockup variants from one prompt, then export PNG and layered PSD files." |
AI tool landing page specificity scorecard
Use this 10-check scorecard before you start a trial, upload files, connect a repo, or ask for budget. Score each row 0, 1, or 2.
| Check | 0 points | 1 point | 2 points |
|---|---|---|---|
| 1. Buyer | Only says "teams" or "creators". | Names a broad function. | Names a role, job, and context. |
| 2. Trigger | No clear moment to use it. | Mentions a general use case. | Shows the exact situation that starts the workflow. |
| 3. Input | Input is hidden or magical. | Mentions prompts, files, or integrations. | Shows examples of required inputs. |
| 4. Output | Only claims productivity. | Mentions an output type. | Shows a sample artifact, screenshot, API response, pull request, or report. |
| 5. Workflow fit | Looks like a generic prompt box. | Explains one step. | Shows before, during, review, and handoff steps. |
| 6. Evidence | No proof beyond adjectives. | Uses testimonials or logos. | Shows realistic examples, docs, changelog, demos, benchmarks, or case studies. |
| 7. Limits | No visible constraints. | Hints at limits in docs. | States what is unsupported, risky, slow, or requires human review. |
| 8. Data trust | No data policy near the claim. | Links to privacy or security docs. | Explains storage, training, retention, permissions, and enterprise controls in buyer language. |
| 9. Cost clarity | No pricing path. | Has a pricing page but important limits are hard to find. | Helps buyers estimate likely cost and points to official pricing. |
| 10. Exit and control | No export, approval, logs, or undo. | Mentions admin or review features. | Shows approval, logs, export, version history, undo, or human-in-the-loop controls. |
Trust checks I would not skip
Specificity is not just a copywriting issue. It tells me whether I can safely test the product with money, files, code, customers, or workflow control.
| Trust question | What the page should make easy |
|---|---|
| Can I test it safely? | A small trial path that does not require sensitive data on day one. |
| Can I verify the claim? | Docs, examples, demo output, changelog, or an official help center link. |
| Can I estimate the bill? | Official pricing link, usage language, and clear warnings when limits can vary. |
| Can I recover from mistakes? | Approval gates, undo, export, logs, version history, or human review. |
| Can I explain it to my team? | A one-sentence job-to-be-done that a non-buyer can understand. |
The copy shift I want to see
These examples are simple on purpose. The useful version is not prettier; it is easier to evaluate.
| Vague version | Specific version | Why the specific version is better |
|---|---|---|
| "Your AI teammate for everything." | "Connect a GitHub repo, ask for a refactor plan, review the diff, then approve or reject the pull request." | Names input, output, and human review. |
| "Know your customers with AI." | "Paste 50 support tickets and get a tagged complaint report with source quotes and confidence labels." | Shows the artifact and evidence trail. |
| "Research faster with agents." | "Search five official sources, list citations, mark unsupported claims, and export a brief for review." | Defines source handling and verification. |
| "Automate your content workflow." | "Turn one product changelog into a blog outline, three social drafts, and a publish checklist." | Turns a broad promise into concrete deliverables. |
| "Secure AI for your company." | "Keep prompts and files out of model training by default, require SSO, and show audit logs for admin review." | Translates security into controls a buyer can check. |
Real pages I use to calibrate the test
These are not endorsements or rankings. I use them as calibration examples: what does the official page make easy to understand, and what still needs checking before buying?
| Tool | Specificity signal worth noticing | What to still verify |
|---|---|---|
| ChatGPT | The official page frames ChatGPT as an everyday chatbot for exploring ideas, solving problems, and learning faster. | Which plan, model access, data controls, file limits, and workspace features match your use case. |
| Claude | Anthropic's docs position Claude around language, reasoning, analysis, and trustworthiness rather than only a generic prompt box. | Current model access, context limits, team controls, data policy, and official pricing. |
| Cursor | Cursor's product page gives a concrete coding workflow: agents that plan, write, and review code across desktop, CLI, web, and mobile surfaces. | Repo permissions, code review controls, team privacy settings, usage limits, and pricing. |
| Perplexity | Perplexity describes itself as an AI answer engine for accurate, trusted, real-time answers, which is more specific than "AI search" alone. | Source quality, citation handling, model choice, premium data access, privacy, and plan limits. |
| OpenAI API platform | The API page separates build, ground, and act: create agent workflows, add context through tools, and connect actions across systems. | Which APIs, tools, safety controls, rate limits, and pricing rules apply to your workload. |
Submit your landing page for review
AI Picker can review a small number of AI tool landing pages as research examples. This is not a paid placement, not a guarantee of coverage, and not a product endorsement.
Submit your landing page for review
Why this is worse with AI tools
Traditional software often exposes its function through menus and screenshots. AI tools can hide behind language. A prompt box can claim to do anything, so specificity is the substitute for a product tour. The more general the promise, the more you should demand concrete artifacts.
This does not mean broad platforms are bad. Chat assistants, coding agents, and model APIs are broad by design. But even broad products need specific examples of tasks, limits, controls, and costs. A broad tool with clear examples is different from a vague tool with expensive ambition.
My final buyer question
Before you create an account, ask: "What manual step would I stop doing if this worked?" If the landing page cannot help you answer that, the product is not yet a purchasing decision. It is only a curiosity.