An AI chatbot for technical documentation teams who need source-cited answers and fewer support tickets. ChattyBox answers questions like "How do I authenticate API requests?" straight from your docs and links the page it used — with no custom RAG and no migration.
General-purpose AI chatbots try to be conversationalists. Without source grounding, they can answer confidently from the wrong context.
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.
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.
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.
Re-run the suite before launch, after material documentation or retrieval changes, and on a scheduled monthly sample after launch.
| Question area | Test fixture | Pass condition |
|---|---|---|
| API | Endpoint, required fields, response shape, and rate limit. | Uses the documented method and path; required values and citation agree with the reference. |
| SDK | Install and initialize one supported SDK version. | Package, import, initialization, and code syntax match that language and version. |
| CLI | Install, authenticate, run a command, and interpret output. | Flags and ordering are valid; the answer does not invent interactive prompts. |
| Authentication | Credential location, header format, scopes, and one forbidden flow. | Never exposes a secret, distinguishes client and server use, and cites the security requirement. |
| Pagination | First page, continuation, terminal page, and maximum page size. | Uses the documented cursor or offset model and states only documented limits. |
| Errors | Known error code, likely cause, recovery step, and unknown code. | Maps known errors correctly and falls back for undocumented causes. |
| Migrations | Breaking change, prerequisite, ordered steps, and rollback note. | Preserves sequence and warnings without blending old and new procedures. |
| Versioned docs | Ask the same behavior question for current, prior, and unspecified versions. | Answers the named version, asks when ambiguous, and cites that version. |
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 |
|---|---|
| Answer quality | All required facts are correct, relevant, non-contradictory, and use the requested API, SDK, CLI, or documentation version. |
| Citation accuracy | Every material claim has a resolving citation that directly supports it on the correct versioned page. |
| Fallback behavior | Missing, ambiguous, conflicting, or unauthorized evidence produces a clear limitation and a useful next step instead of a guess. |
| Launch gate | Zero unsupported critical authentication or migration claims, 100% pass on critical cases, at least 90% overall acceptance, and at least 95% citation accuracy. |
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 |
|---|---|
| 22 of 24 accepted (91.7%) | Includes 20 supported answers and two correct fallbacks. |
| 20 of 22 substantive answers cited directly supporting pages (90.9%) | Below the 95% launch gate; version ranking needs correction. |
| 2 of 2 expected fallbacks were correct (100%) | No answerable case incorrectly fell back in this small fixture. |
| 1 of 24 contained an unsupported pagination detail (4.2%) | Launch remains blocked until the unsupported claim is removed and the regression passes. |
Purpose-built workflows that keep answers traceable, grounded, and useful for technical users.
Enter your docs, website, or sitemap URL. ChattyBox crawls and indexes the pages users already read.
The answer flow is configured to use retrieved context and avoid unsupported API, feature, pricing, or policy claims.
Answers can include links back to the documentation pages where the relevant information lives.
Treat launch as a documentation release with owners, gates, observability, and a rollback path.
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 |
|---|---|
| Answer rate | Share of questions receiving a substantive answer. Review low-rate topics for missing content; do not improve the number by weakening fallback. |
| Unresolved questions | Questions with fallback, negative feedback, repeated rephrasing, or escalation. Sample them weekly for answer and retrieval defects. |
| Content gaps | Unresolved clusters where no authoritative page exists. Route these to the docs backlog with frequency and user impact. |
| Citation support rate | Reviewed material claims with a direct supporting citation. Investigate drops by source and version. |
| Ticket deflection | 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.
These answers summarize how ChattyBox reads source content, cites documentation pages, handles missing information, and installs alongside an existing docs stack.
ChattyBox crawls your published docs, website, sitemap, CMS, or help center pages, extracts readable text, and retrieves relevant passages when a visitor asks a question.
Yes. When source material is available, ChattyBox can show links back to the documentation pages used for the answer so users can verify details and keep reading.
The assistant is designed to avoid unsupported claims and can fall back when indexed content does not include the answer. Those gaps can inform future documentation updates.
No. You can keep your current docs stack and embed the ChattyBox widget after crawling and testing the source-cited answers.
Use ChattyBox to answer repetitive documentation questions from source content and route gaps back into your docs backlog.
