Technical Writing Blog
ClickHelp — online documentation tool for technical writers and teams. Create, translate, and publish documentation easily in one portal! Get your FREE trial 👉 https://goo.gl/NahsT2
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Подписчиков у канала «Technical Writing Blog»: 1 256.
По дням, последние 30 дней
| Дата | Подписчики | Изменение | Посты | Ср. просмотры поста |
|---|---|---|---|---|
| 12 ноя | 1 256 | −1 | 0 | – |
| 11 ноя | 1 257 | 0 | 1 | 39 |
| 10 ноя | 1 257 | 0 | 0 | – |
| 9 ноя | 1 257 | −3 | 0 | – |
| 17 окт | 1 260 | – | 0 | – |
Когда публикует
Время по UTC. 15 постов за 90 дней.
Последние посты
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AI already drafts documentation, edits it, and answers readers on a company’s behalf. When it gets something wrong, “the AI did it” does not work as an explanation, for customers or in court. Responsibility for the content stays with people. The question is which people. In our article, we suggest a governance model for AI-assisted docs. Errors can enter at five stages, from an outdated source page to a chatbot that misreads correct docs, and each stage needs an owner. The model defines four roles and matches human oversight to how independently AI works and how costly an error would be. A typo fix and a security instruction should not go through the same review. The article also covers wh...
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The November 2026 update is out, and much of it is about making changes with confidence. Say you need to replace an image used across a large portal. Where is this file used?, Global Find/Replace, and Link Viewer find every topic that links to it in a couple of seconds, even on portals with hundreds of thousands of topics. And before you delete a project or publication, ClickHelp checks your other projects for links to it. If you’ve been holding off on the new Reader UI, most classic template settings now work there too, so your portal keeps its look. Readers also get AI Overview in search and can connect your docs to their AI assistant through MCP. For teams that share restricted docs wi...
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Many teams that “use AI for docs” spend part of their day copying text from a chat window into the portal. At that point the writer is the one assisting the AI. In our article, this is L1 on a five-level scale of AI autonomy in documentation. The scale starts at L0, where AI only suggests phrasing as you type, and ends at L4, where it monitors the product and publishes updates without approval. Almost no one is at L4 today. The middle of the scale is where it gets harder. Moving from L1 to L2 is an integration task: the AI gets into the portal and sees the structure, the existing topics, and the workflow statuses. Moving from L2 to L3 is a governance task. Someone has to decide which conte...
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Your docs chatbot gives a wrong answer, and the usual fix is a bigger model. We tested that. It didn’t help. We ran 50 questions our users had actually asked through 14 configurations, all on the same model. Often the model simply never got the article with the answer. No model, however big, can answer from an article it never saw. A reranker didn’t help either: no improvement in four comparisons. What did help was the work around the model: how the docs are prepared for search, how much context the model gets, and what it does when it isn’t sure. Read the full test and a five-step check for your own docs
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🤖 Your AI support assistant has a 78% deflection rate. Is that a success? Not necessarily. Some users get the answers they need. Others receive an unhelpful response, give up, and open a support ticket the next day. On your dashboard, both scenarios can look exactly the same. That’s why deflection rate alone isn’t enough to evaluate an AI documentation system. 📊 Here are 5 metrics that really matter: ✅ Accuracy 📚 Groundedness 🔗 Citation quality 💬 True deflection 💰 Cost per accurate answer The last point is especially important: a cheap AI response can become expensive if it creates more work for your support team. The goal isn’t to make conversations end. It’s to help users find r...
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Imagine reading a single page torn out of the middle of a manual. The instructions make sense, but you can’t tell which product they’re for, which version, or who they were written for. An AI assistant gets your documentation in the same state. Before indexing, every article is cut into fragments of a few hundred tokens, and each fragment is searched on its own. The “Enterprise only” line stays at the top of the article. The steps end up in another chunk. So the useful question isn’t “is the text correct?” It’s “does every fragment know where it came from?” Product, version, audience, task type and environment have to live in structured fields and be copied to every chunk, not mentioned on...
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AI makes writing documentation faster. It doesn’t make it more consistent. Ask an assistant to explain the same step in five topics, and you get five slightly different versions of one instruction. Single-sourcing fixes that: a fragment lives in one place and is reused everywhere. The setup used to be the hard part, because someone had to find every copy by hand. On October 15 we’ll hand that job to AI. An assistant connected to ClickHelp through MCP will go through a project with repeated content, turn those passages into snippets and replace the copies in each topic. We’ll also show how variables keep product versions in check. October 15, 18:00 CEST / 12:00 PM EDT Register for the we...
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A new product edition usually starts with a copied manual. It's fast, and for one copy it works fine. The trouble comes later. The support address changes, and now it has to be found in four manuals and two languages. One copy gets missed, and a user calls the wrong number. Single-sourcing keeps one content base and builds every manual from it. Shared procedures are written once as reusable topics. Product names, versions, and contacts go into variables. Enterprise-only sections are handled with conditional content, and warnings or legal notices live in snippets. The approach has limits. Too many conditions make a topic hard to read, and if two products share less than 50–60% of their con...
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A company ships its second product, and the docs team starts arguing: one site for everything, or a separate site for each product? Both camps have a point. Split the docs, and legal pages, security policies, and shared how-tos get maintained twice. Merge them, and readers of one product keep landing on another product's pages. The argument goes in circles because layout alone guarantees nothing. A more useful test: do readers always know where they are, and can the team avoid maintaining the same content twice? Seen that way, "separate for readers" and "shared for the team" are two independent settings, and most of the trade-off disappears. In the article, we look at four setups teams u...
286 просмотров Фото -
Your documentation calls it "SSO configuration." Your reader types "can't log in with my work account." It's the same problem. But keyword search sees two unrelated phrases, and the reader leaves without an answer. In the September release, AnswerGenius started searching by meaning as well as by keywords. When readers describe a problem in their own words, AnswerGenius still finds the right topic and answers from it. The answers themselves are now written by a newer AI model. Also in this release: Documentation Sites on a ClickHelp subdomain with nothing to set up on your side, Restricted publications in the Published Docs MCP Server, and five features out of beta. Read the September Rel...
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Imagine reading a single page torn out of the middle of a manual. The instructions make sense, but you can’t tell which product they’re for, which version, or who they were written for. An AI assistant gets your documentation in the same state. Before indexing, every article is cut into fragments of a few hundred tokens, and each fragment is searched on its own. The “Enterprise only” line stays at the top of the article. The steps end up in another chunk. So the useful question isn’t “is the text correct?” It’s “does every fragment know where it came from?” Product, version, audience, task type and environment have to live in structured fields and be copied to every chunk, not mentioned on...
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Your docs chatbot gives a wrong answer, and the usual fix is a bigger model. We tested that. It didn’t help. We ran 50 questions our users had actually asked through 14 configurations, all on the same model. Often the model simply never got the article with the answer. No model, however big, can answer from an article it never saw. A reranker didn’t help either: no improvement in four comparisons. What did help was the work around the model: how the docs are prepared for search, how much context the model gets, and what it does when it isn’t sure. Read the full test and a five-step check for your own docs
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🤖 Your AI support assistant has a 78% deflection rate. Is that a success? Not necessarily. Some users get the answers they need. Others receive an unhelpful response, give up, and open a support ticket the next day. On your dashboard, both scenarios can look exactly the same. That’s why deflection rate alone isn’t enough to evaluate an AI documentation system. 📊 Here are 5 metrics that really matter: ✅ Accuracy 📚 Groundedness 🔗 Citation quality 💬 True deflection 💰 Cost per accurate answer The last point is especially important: a cheap AI response can become expensive if it creates more work for your support team. The goal isn’t to make conversations end. It’s to help users find r...
-
Many teams that “use AI for docs” spend part of their day copying text from a chat window into the portal. At that point the writer is the one assisting the AI. In our article, this is L1 on a five-level scale of AI autonomy in documentation. The scale starts at L0, where AI only suggests phrasing as you type, and ends at L4, where it monitors the product and publishes updates without approval. Almost no one is at L4 today. The middle of the scale is where it gets harder. Moving from L1 to L2 is an integration task: the AI gets into the portal and sees the structure, the existing topics, and the workflow statuses. Moving from L2 to L3 is a governance task. Someone has to decide which conte...
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The November 2026 update is out, and much of it is about making changes with confidence. Say you need to replace an image used across a large portal. Where is this file used?, Global Find/Replace, and Link Viewer find every topic that links to it in a couple of seconds, even on portals with hundreds of thousands of topics. And before you delete a project or publication, ClickHelp checks your other projects for links to it. If you’ve been holding off on the new Reader UI, most classic template settings now work there too, so your portal keeps its look. Readers also get AI Overview in search and can connect your docs to their AI assistant through MCP. For teams that share restricted docs wi...
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AI already drafts documentation, edits it, and answers readers on a company’s behalf. When it gets something wrong, “the AI did it” does not work as an explanation, for customers or in court. Responsibility for the content stays with people. The question is which people. In our article, we suggest a governance model for AI-assisted docs. Errors can enter at five stages, from an outdated source page to a chatbot that misreads correct docs, and each stage needs an owner. The model defines four roles and matches human oversight to how independently AI works and how costly an error would be. A typo fix and a security instruction should not go through the same review. The article also covers wh...
Вопросы о канале «Technical Writing Blog»
Сколько подписчиков у канала «Technical Writing Blog»?
У канала «Technical Writing Blog» (@clickhelp_technicalwritingblog) 1 256 подписчиков на 12 ноября 2026 г.
О чём канал «Technical Writing Blog»?
Канал в категории «Блоги», язык постов: английский. Описание канала: «ClickHelp — online documentation tool for technical writers and teams. Create, translate, and publish documentation easily in one portal! Get your FREE trial 👉 https://goo.gl/NahsT2»
Какое место занимает канал «Technical Writing Blog»?
По числу подписчиков: №38 в категории «Блоги», №7 135 среди каналов на языке «английский», №8 034 в мире.
Публикует ли канал «Technical Writing Blog» рекламу?
За последние 30 дней ни один из 6 постов не помечен как реклама (#ad, Sponsored, «Реклама»). Рекламу без пометки определить нельзя.
Когда публикует канал «Technical Writing Blog»?
Пик публикаций: среда, около 11:00 по UTC. Выходит около 1 поста в неделю.
Статистика обновлена 15 минут назад.