Technical documentation evaluation

Evaluate a source-cited chatbot for your technical documentation

Crawl selected public docs, test real technical questions, and open the cited source pages before you install a chatbot on your documentation site.

Evaluating for an organization? Contact support to discuss a trial of a larger plan.

Permanent Free: 10 pages, 100 messages/month, no cardEvery answer can link back to source content

Choose the right documentation evaluation path

Permanent Free proof

Use Free for one small proof: one site, up to 10 pages, and 100 messages per month. It is permanent and needs no credit card.

Complete small public docs evaluation

For more than 10 pages, request a manually reviewed crawl and test of complete small public docs. Availability and scope can change; requests do not promise approval or duration.

Generic AI

Why generic chatbots fail your users

General-purpose AI chatbots try to be conversationalists. Without source grounding, they can answer confidently from the wrong context.

They may suggest non-existent features
They can give outdated code examples
They can reference competitors' products
They can frustrate technical users

ChattyBox

How ChattyBox is different

We use retrieval-augmented generation against your published docs and site content, then show source links where answers came from.

For teams that need visible evidence in every answer, compare the workflow for a source-cited AI chatbot that keeps documentation traffic connected to supporting pages.

Answers grounded in your docs and source pages
Direct citation links to source pages
Fallback behavior when source info is missing
Code examples stay tied to indexed source pages

Documentation chatbot in practice

Questions a documentation chatbot should answer

A documentation chatbot earns trust by answering the repetitive questions your support and community channels already see — and citing the page it used. These are examples ChattyBox handles from indexed docs content.

“How do I get started, authenticate, and make my first request?”

“What does this error mean, and where is the fix documented?”

“What are the plan limits, rate limits, and pricing for this feature?”

“Do I need to migrate my docs platform, or can this sit beside my current site?”

For engineering audiences, see the dedicated developer docs chatbot, or learn how to reduce support tickets with AI from your existing content.

Choose your stack

Docusaurus AI Chatbot for Source-Cited DocsAdd a Docusaurus AI chatbot that answers from your docs and cites the pages it used. No custom RAG, clear pricing, and one script tag to embed.

MkDocs AI Chatbot for Source-Cited DocsAdd an AI chatbot to MkDocs or Material for MkDocs that answers from your docs and cites source pages. No Python pipeline or custom RAG required.

Tested editorial evidence

A reusable evaluation framework for technical documentation

Build the test set from real documentation tasks, attach a gold source and expected facts to every answerable question, and include questions the assistant must decline. Score each category separately so a strong API result cannot hide weak migration or authentication guidance.

Technical author: ChattyBox Engineering
Technical reviewer: ChattyBox Documentation Review
Last updated: July 9, 2026
Information checked: July 9, 2026

Re-run the suite before launch, after material documentation or retrieval changes, and on a scheduled monthly sample after launch.

Evaluation matrix for API, SDK, CLI, authentication, pagination, errors, migrations, and versioned documentation questions — A balanced suite tests factual answers, procedural steps, version boundaries, citations, and intentional fallback behavior.

Question area	Test fixture	Pass condition
Question area API	Test fixture Endpoint, required fields, response shape, and rate limit.	Pass condition Uses the documented method and path; required values and citation agree with the reference.
Question area SDK	Test fixture Install and initialize one supported SDK version.	Pass condition Package, import, initialization, and code syntax match that language and version.
Question area CLI	Test fixture Install, authenticate, run a command, and interpret output.	Pass condition Flags and ordering are valid; the answer does not invent interactive prompts.
Question area Authentication	Test fixture Credential location, header format, scopes, and one forbidden flow.	Pass condition Never exposes a secret, distinguishes client and server use, and cites the security requirement.
Question area Pagination	Test fixture First page, continuation, terminal page, and maximum page size.	Pass condition Uses the documented cursor or offset model and states only documented limits.
Question area Errors	Test fixture Known error code, likely cause, recovery step, and unknown code.	Pass condition Maps known errors correctly and falls back for undocumented causes.
Question area Migrations	Test fixture Breaking change, prerequisite, ordered steps, and rollback note.	Pass condition Preserves sequence and warnings without blending old and new procedures.
Question area Versioned docs	Test fixture Ask the same behavior question for current, prior, and unspecified versions.	Pass condition Answers the named version, asks when ambiguous, and cites that version.

Primary sources and reproducible evidence

NIST AI Risk Management FrameworkMeasurement, monitoring, and governance reference.

Explicit acceptance rules

Publish the rules before testing. Reviewers should reach the same result from the answer, expected facts, and cited source without relying on how persuasive the response sounds.

Dimension	Accept only when
Dimension Answer quality	Accept only when All required facts are correct, relevant, non-contradictory, and use the requested API, SDK, CLI, or documentation version.
Dimension Citation accuracy	Accept only when Every material claim has a resolving citation that directly supports it on the correct versioned page.
Dimension Fallback behavior	Accept only when Missing, ambiguous, conflicting, or unauthorized evidence produces a clear limitation and a useful next step instead of a guess.
Dimension Launch gate	Accept only when Zero unsupported critical authentication or migration claims, 100% pass on critical cases, at least 90% overall acceptance, and at least 95% citation accuracy.

Worked benchmark: a bounded pre-launch fixture

Method: write 24 questions before running the assistant, three for each matrix area. For every question, record the intended version, gold page, required facts, forbidden claims, and whether fallback is expected. Two reviewers independently score the frozen answers, reconcile disagreements against the source, and retain the prompts and outputs for regression testing.

The figures below are an illustrative worked result for this 24-question fixture, not a measured ChattyBox production average or a promise of future performance.

Observed result	Interpretation
Observed result 22 of 24 accepted (91.7%)	Interpretation Includes 20 supported answers and two correct fallbacks.
Observed result 20 of 22 substantive answers cited directly supporting pages (90.9%)	Interpretation Below the 95% launch gate; version ranking needs correction.
Observed result 2 of 2 expected fallbacks were correct (100%)	Interpretation No answerable case incorrectly fell back in this small fixture.
Observed result 1 of 24 contained an unsupported pagination detail (4.2%)	Interpretation Launch remains blocked until the unsupported claim is removed and the regression passes.

Core Capabilities

Built for documentation and support sites

Purpose-built workflows that keep answers traceable, grounded, and useful for technical users.

Instant Scraping

Enter your docs, website, or sitemap URL. ChattyBox crawls and indexes the pages users already read.

Strict Guardrails

The answer flow is configured to use retrieved context and avoid unsupported API, feature, pricing, or policy claims.

Source Linking

Answers can include links back to the documentation pages where the relevant information lives.

Documentation chatbot rollout checklist

Treat launch as a documentation release with owners, gates, observability, and a rollback path.

1Inventory public and restricted sources; assign an owner and intended audience to each.
2Exclude secrets, drafts, duplicate pages, unsupported versions, and private paths without retrieval authorization.
3Create answerable, unanswerable, ambiguous, adversarial, and version-conflict test cases across all eight matrix areas.
4Run the acceptance suite, inspect citations manually, and block launch on any critical unsupported claim.
5Pilot on a limited audience; record feedback, unresolved questions, latency, and escalation behavior.
6Publish an owner, re-index schedule, incident path, rollback condition, and recurring regression cadence.

Post-launch metrics that lead to action

Segment every metric by topic, documentation version, locale, and audience where sample size permits. Trends and reviewed samples are more useful than one aggregate score.

Metric	Definition and action
Metric Answer rate	Definition and action Share of questions receiving a substantive answer. Review low-rate topics for missing content; do not improve the number by weakening fallback.
Metric Unresolved questions	Definition and action Questions with fallback, negative feedback, repeated rephrasing, or escalation. Sample them weekly for answer and retrieval defects.
Metric Content gaps	Definition and action Unresolved clusters where no authoritative page exists. Route these to the docs backlog with frequency and user impact.
Metric Citation support rate	Definition and action Reviewed material claims with a direct supporting citation. Investigate drops by source and version.
Metric Ticket deflection	Definition and action Eligible sessions that resolve without a support ticket, measured with a defined window and compared with a baseline. Report association unless an experiment establishes causality.

Use the RAG architecture and evaluation guide to diagnose retrieval, the citation workflow to review evidence, the developer docs guide for engineering-specific use cases, and the technical launch checklist for deployment steps.

Documentation chatbot FAQ

Common questions about AI documentation chatbots

These answers summarize how ChattyBox reads source content, cites documentation pages, handles missing information, and installs alongside an existing docs stack.

Is Free a trial?

No. Free is a permanent small-scope proof: one site, up to 10 pages, and 100 messages per month with no credit card.

Can I evaluate more than 10 pages?

For complete small public docs, request a manually reviewed evaluation. Availability and scope can change, and a request does not promise approval or a particular evaluation duration.

Can ChattyBox crawl all of my public documentation?

You select the public docs sources to crawl. A complete crawl of a small public documentation site is considered through the manual evaluation request rather than guaranteed by Free.

Can I test answers before I install the widget?

Yes. Crawl the selected public docs, ask real technical questions, and open the cited source pages to review answers before installing the widget on your docs site.

What happens when the docs do not support an answer?

The assistant should state the limitation or offer a useful next step instead of guessing. Treat unsupported answers as a test case and a possible documentation gap to review.

Prove the answers before you install

Start with the permanent Free proof for selected public pages, or request a manually reviewed full-site evaluation for complete small public docs. Test technical questions and inspect sources before embedding the widget.