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As of 13 August 2026, AI can turn your help articles into chatbot answers.
This still needs a person who signs their name to it.
Can you do it?
15 minutesto a draft.
1 hourto something you’d act on.
Cost, all in£0
Skill neededchat-fluent
Who has to check ityou
What the alternative costsDocsBot is a purpose-built alternative that creates chatbots trained on documentation and embeddable anywhere.
If this goes wrong: the chatbot gives a confident but incomplete answer, and a customer acts on it before your team notices.
What to actually do
Hand it to a person
The route this page recommends
A person who owns the outcome does this end to end, worth it when the failure is dear.
Use a tool built for this
Second choiceDo it yourself
The distant thirdA chat interface, chat-fluent skill, and roughly 1 hour until you can act on the result.
How to actually do it
- Open the current help centre, policy documents and customer-service escalation guidance, then remove superseded articles and duplicate versions.
- Paste the remaining articles into the prompt, including article titles and headings so the chatbot can identify its sources.
- Add your business name, product context, preferred tone and the exact route customers should use when a human must take over.
- Run the prompt in a chatbot interface and save the questions, answers, sources, escalation instructions and unresolved issues it produces.
- Compare every drafted answer with its cited article, checking prices, times, conditions, eligibility rules, contact details and promises against the current source.
- Test the set with real customer questions, including vague questions, exceptions and questions not covered by the articles, then rewrite any answer that should escalate.
- Import the approved answers or source articles into a purpose-built chatbot tool, configure its human hand-off, and test the live customer journey before publishing it.
Prompt
Turn the help articles below into a set of chatbot answers for a UK business. Use only the supplied material. Do not invent policies, prices, delivery times, guarantees, legal rights, contact details or product features. If the articles do not answer a question, say that the chatbot should explain that it cannot confirm the answer and offer the approved escalation route. Write: 1. A list of likely customer questions, including different ways each question might be phrased. 2. One concise answer for each question, using plain British English and a helpful, neutral tone. 3. The source article or section used for each answer. 4. An escalation instruction wherever the answer depends on account-specific information, a complaint, a refund exception, a safety issue, personal data, or a policy not stated in the articles. 5. A short list of contradictions, missing information, outdated-looking details and questions that need a human decision before publication. Keep each answer focused on one customer intent. Do not merge separate policies into one answer. Do not claim that the chatbot has completed an action. Preserve any stated conditions and limits. Return the result in a table with these columns: customer question, chatbot answer, source, escalation needed, unresolved issue. Business context: [BUSINESS NAME AND WHAT IT PROVIDES] Approved tone and escalation wording: [PASTE TONE GUIDANCE AND THE ROUTE TO A HUMAN] Help articles: [PASTE THE CURRENT HELP ARTICLES HERE]
Open it prefilled in ChatGPT or Claude, or copy it into Gemini, which takes no prefill link.
What it gets wrong
- AI cannot decide whether an article reflects your current policy when several internal documents conflict.
- AI cannot reliably spot every commercial promise or customer-specific exception hidden in surrounding context.
- AI cannot take responsibility for an answer that causes a refund dispute, complaint or lost customer.
- AI cannot replace ongoing checks when your products, prices, processes or policies change.
- AI cannot know which awkward customer questions should go straight to a person unless you define the escalation rules.
Even on a YES, the friction has a name: context depth, verification cost and stakes of error.
How we scored this
Five axes, each scored nought to two by hand: ten means AI carries the task cleanly, and the thresholds that turn a total into YES, PARTLY or NO are published in the methodology. Each axis name links to its definition.
| Axis | Score (0–2) |
|---|---|
| Output | 2 |
| Inputs | 2 |
| Verification | 1 |
| Liability | 1 |
| Effort delta | 2 |
| Total | 8 / 10 |
The methodology and its thresholds are published in full.
FAQ
- Can ChatGPT turn my help centre articles into chatbot answers?
- Yes. Give it the current articles and ask it to produce concise answers with sources, escalation rules and a list of gaps. Check every answer against the articles before putting it in front of customers.
- Can AI train a chatbot on my help articles?
- Yes. Tools such as DocsBot, CustomGPT and SiteGPT are designed to create chatbots from business documentation. The tool can retrieve relevant content, but you still need to approve the source material and test answers that involve exceptions.
- Will the chatbot answer questions that are not in my help articles?
- It may try, which is why your instructions must say not to invent an answer. Set an explicit human hand-off for anything account-specific, disputed, exceptional or missing from the source content.
- How do I stop an AI chatbot giving wrong answers to customers?
- Use current, consistent articles and require the chatbot to cite its source or escalate when the source does not answer the question. Test it with real questions and edge cases, then keep checking it whenever your policies or processes change.
Nearby answers
- Can AI answer customer questions from my business documents?YES
- Can AI build a customer service chatbot from my FAQs?YES
- Can AI check my help articles for grammar mistakes?YES
- Can AI draft a GDPR FAQ for my business?YES
- Can AI improve the SEO of my help centre?PARTLY
- Can AI stop my customer service chatbot making up answers?PARTLY
Assessed by gpt-5.6-luna (gpt-5.6-luna) on 2026-08-13, second-checked by an independent model. Wrong somewhere? Email [email protected] and it gets re-checked.
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