The AI Instruction Insider May 2025

How tech writers can use Google Gemini 2.5 to learn (and work) 10x faster

What is the AI news?

The AI world is moving fast - but so can you. AI is evolving fast – but you don’t have to be left behind. Google’s Gemini 2.5 makes it easier than ever to keep up, especially if you work with complex content. From dense manuals to long video transcripts, it can process massive volumes of information and turn it into something clear and usable in seconds.

Let’s break down how Gemini 2.5 can make you not just a faster learner, but a smarter and more efficient technical writer.

Why it matters

Recording product demos with SMEs: Gemini as your video sidekick

What’s the scenario?
You’re filming a product walk-through with a subject matter expert (SME). It’s full of jargon, nuanced explanations, and system logic that your audience needs in a clean, structured document.

How Gemini 2.5 helps:
Upload the recorded video (or transcript) directly into Gemini. Then prompt it to extract key points, create a how-to guide, or even generate FAQs from the content.

Example prompt:

“This video shows a 40-minute SME-led demo of our new warehouse automation system. Turn this into a technical overview with process steps, key terminology definitions, and setup instructions. Include bullet points and user-friendly formatting.”

How to do it step-by-step:

1. Upload your video or transcript to Gemini.
2. Ask for a structured output (manual, checklist, setup guide).
3. Follow up with prompts like “Rewrite this for end-users” or “Add troubleshooting steps based on this video.”

Analyzing long supplier documentation (without going cross-eyed)

What’s the scenario?
Imagine getting a 200-page PDF from a third-party supplier with detailing equipment specs or obscure safety regulations. 

How Gemini 2.5 helps:
Feed the entire document into Gemini. Ask for summaries, comparisons with other documentation, or help translating engineering jargon into plain language.

Example prompt:

“Here’s the supplier manual for our new industrial filter system. Highlight all required maintenance steps, safety warnings, and component specs. Group them by function.”

Bonus prompt:

“Explain the critical specs from this manual as if I need to write a one-page quick-start guide for new technicians.”

Five practical strategies tech writers can use with Gemini 2.5

1. Turn vomplex content into courses

Upload research papers, API docs, or system specs, and ask Gemini to turn them into chapter-based learning modules.

Prompt: “Break this documentation into 5 chapters, each with a summary, key terms, and quiz questions.”

2. Compare competing tech docs

Need to contrast two vendors’ systems? Upload both sets of docs.

Prompt: “Compare these two product guides. What are the differences in functionality, integration steps, and target users?”

3. Simplify the complex

Ask for analogies and plain-English explanations.

Prompt: “Explain this Kubernetes networking section like I’m onboarding a junior writer.”

4. Generate style-aligned content

You can feed Gemini your own past documentation and ask it to match your tone, structure, or format.

Prompt: “Rewrite this section from the supplier doc using the tone and format of our onboarding manual.”

5. Create personalized learning paths

Want to upskill in a topic? Upload a manual or book, then ask Gemini to build a study plan with daily tasks and exercises.

Prompt: “Here’s a 150-page manual on our internal tooling. Make a 7-day learning plan with checkpoints and mini-quizzes.”

How it helps you

Pro tips to master Gemini

• Talk to it like a teammate: Be clear and specific. Instead of vague asks, request bullet points, side-by-side comparisons, or plain-language explanations. 
• Need a book summarised? Ask for chapter breakdowns. 
• Keep the conversation going: Treat Gemini like a brainstorming partner. Follow up with “Can you simplify that?” or “Give me three examples.” 
• Dig deeper with second and third questions to refine results.

Gemini 2.5 Flash

Just weeks after launching Gemini 2.5 Pro, Google has followed up with Gemini 2.5 Flash, a faster, cheaper, and highly flexible model that marks the company’s full takeover of the AI landscape. It has high-level performance, speed, and affordability, so Google now sets the benchmark across the board.

And what makes Gemini 2.5 Flash so unique? It's all about hybrid reasoning: developers can dial up or down how much the model “thinks,” offering a rare blend of speed and depth. It's powered with Google's massive user base, proprietary infrastructure.

There is also a vast amount of data from products like Search, YouTube, and Gmail - allowing the company to dominate every layer of the AI stack. As tech analyst Alberto Romero says: “Google is now winning on all fronts.” The AI race isn’t about who will win - but how far ahead Google will finish. 

Final thoughts & Google’s undeniable AI domination

Gemini 2.5 is more than a fancy summariser. Technical writers now have a powerful assistant to boost clarity, speed, and accuracy - no matter the task. Just weeks after launching Gemini 2.5 Pro, Google followed up with Gemini 2.5 Flash - a faster, cheaper, and highly flexible model that marks the company’s full takeover of the AI landscape.

With top-tier performance, speed, and cost-efficiency, Google now leads in every key category. With top-tier performance, speed, and cost-efficiency, Google now leads in every key category. Google’s AI is everywhere now - not just chatbots. They’ve built top-tier tools for images (Imagen 3), video (Veo 2), speech (Chirp 3), and music (Lyria).

Combined with their massive user base, Search/YouTube/Gmail data, and powerful tech, Google’s leading the AI race.

This AI researcher predicts superintelligence by 2027

A former OpenAI safety researcher who walked away from millions is now making bold predictions: we could see superintelligent AI by 2027. So, what does he know that we don’t?

What is the AI news?

Daniel Kokotajlo, an ex-OpenAI researcher known for his accurate AI forecasts and strong stance on transparency, just released an essay titled AI 2027. In it, he outlines a dramatic timeline with other experts, suggesting we’ll go from today’s chatbots to superintelligent AI in just two years.

According to it, we will see capable AI assistants by mid-2025, major breakthroughs in coding by 2026, and by late 2027. Also there will be a system called “Agent-4” that could revolutionize science - or potentially turn against humanity. The key driver? An intelligence explosion - where AI improves itself faster and faster, compressing years of progress into weeks or even days.

The essay went viral in tech circles and was even picked up by The New York Times, partly due to Kokotajlo’s credibility. He famously left OpenAI and declined millions in equity rather than stay silent under an NDA.

Why it matters

If superintelligent AI is even plausible within a few years, it’s a game-changer for how we write, document, and interact with technology. You might think, “Well, I’m just writing docs - does this affect me?” The answer is a resounding yes.

As AI tools advance, your role may change fast - using AI to write, documenting AI systems, or reviewing AI-generated content.

This level of AI advancement also raises ethical and regulatory questions. Technical writers are often the bridge between complex systems and human understanding. If those systems become radically more powerful, your communication skills will be more critical than ever.

How it helps you

Whether or not Kokotajlo’s 2027 prediction proves true, here’s what you can do now:

• Stay ahead of the curve: start exploring advanced AI tools like GPT-4, Claude, and open-source LLMs. Understanding their capabilities will prepare you to use - and critique - them effectively.
• Level up your AI literacy: read papers like AI 2027 or follow summaries from trusted voices (like AI Explained or Astral Codex Ten). Your ability to explain fast-moving tech clearly will only grow in demand.
• Think long-term: consider how exponential change might impact your documentation processes. Would your current style still work if AI systems were your main “readers”? Are you ready to co-author content with an AI?

Superintelligence might not be two years away = but the world is definitely speeding up. Technical writers who understand this landscape will be vital guides in the storm.

A glimpse of hope: Making superintelligence understandable

Amid the dizzying pace of AI development, one startup is taking a radically different approach: Goodfire. With $50 million in fresh funding and a team of former OpenAI and DeepMind researchers, the company is building Ember - a platform designed to make AI models transparent and interpretable.

Why does this matter in the context of superintelligence? Because if systems like Agent-4 emerge, we’ll need to understand how they “think.” Goodfire’s use of mechanistic interpretability - a method akin to giving AI a brain scan - helps reveal which neurons activate during specific tasks. This could be critical as models evolve beyond human-readable text and begin communicating in “neuralese”: faster, denser, but incomprehensible to us.

In a future where AI might think in vectors instead of words, Goodfire’s tools could help humans stay in the loop - ensuring we don’t lose control over the very systems we’re racing to build.

O3: A glimpse of human-level AI?

What is the AI news?

According to the latest news, OpenAI’s newly released “O3” model means nothing less than but the arrival of AI with human intelligence. That’s a bold statement - but it’s fuelling excitement and speculation across tech circles. There are not many official details yet, but  early access users describe O3 as “strikingly humanlike” in conversation, capable of reasoning across complex tasks, and even showing creativity. This isn’t just a smarter chatbot - it might represent a major leap in general intelligence.

Why it matters

If O3 delivers on its promise, it could reshape how we approach technical writing. It’s no longer just about speeding up edits. Think tools that understand your brief, ask smart questions, and help build clear, structured drafts - pulling insights directly from expert input.

This shifts the role of technical writers from content producers to content curators and strategists. Your value will increasingly lie in guiding AI output, ensuring accuracy, and maintaining tone and context - skills that machines still struggle with.

How it helps you

• Let’s get practical. If O3 lives up to the hype, here are a few ways it could soon transform your workflow:
• Content drafting: Need a quick first draft of an API guide or troubleshooting article? O3 might generate highly structured content tailored to your audience.
• Multilingual documentation: O3’s language capabilities are reportedly much improved, enabling faster and more accurate translations with minimal post-editing.
• Voice-to-doc automation: O3 could transcribe, summarise, and convert interviews or technical calls into usable content with minimal intervention.
• Documentation audits: Some testers say O3 can spot logical inconsistencies, missing steps, or unclear phrasing – helping you catch issues before your users do.

The takeaway? We’re entering an era where your AI assistant won’t just suggest synonyms – it could help architect entire documentation systems. That’s a big deal.

Of course, the hype needs to be balanced with reality. O3 isn’t public yet, and OpenAI hasn’t released full benchmarks or technical specifications. But if even half the rumours are true, it’s time for technical writers to stop asking “what can AI do for me?” and start asking “how do I want to collaborate with it?”

DocRock™ and the rise of AI-powered technical documentation: A game-changer for technical writers

What is the AI news?

Some say OpenAI’s new “O3” model points to intelligence on par with humans. While that’s still up for debate, tools like DocRock™ are already showing how this next wave of tech can make a real difference - especially for technical writers. Built by INSTRKTIV, DocRock™ is designed to speed up and simplify the entire documentation process, from setup to final review.This isn’t a minor update. It’s a total rethink of how we create manuals, user guides, and safety instructions - especially in regulated industries like machinery, medical devices, and electrical equipment.

Why it matters

Let’s face it: technical documentation has long been the bottleneck in product development. Manuals take 80-120 hours or even much more to produce. Regulatory standards like ISO 20607 and IEC/IEEE 82079-1 make the process even slower. And because documentation often comes last, it holds up product launches, customer onboarding, and even final payments.

One example from INSTRKTIV: a team of two documentation specialists was supporting over 60 engineers - an impossible ratio. In some companies, engineers themselves draft manuals, which leads to inconsistent terminology, missing safety warnings, and compliance risks.

Enter DocRock™

DocRock™ reimagines the technical writing process using a hybrid AI + human approach. Here’s how it works:

1. Project setup & template selection: Writers start by selecting document types tailored to product categories and regulations.
2. Input data collection: AI ingests everything from legacy docs to SME interviews, even smart-glasses video recordings.
3. AI content generation: Specialised agents (Troubleshooting, Maintenance, Safety) create structured content using compliance-embedded prompts.
4. Human review: Writers refine output in a clean interface that supports real-time feedback and approvals.

The results? Stunning.

In real-world pilots, DocRock has cut manual creation time by 75% - from 3-4 weeks down to just a few days. Teams reported:

• 60% cost reduction in documentation
• Faster compliance with EU regulations (ISO, ANSI, etc.)
• 2.5x increase in capacity for manual production
• Fewer product launch delays
• Better SME collaboration and customer satisfaction

One notable case: for a next-gen ice bath product, a full compliant manual was created in under six days, covering everything from interviews to final output.

How it helps you

Let’s be clear – DocRock™ isn’t about replacing writers. It’s about augmenting them. AI handles the heavy lifting: structuring, drafting, and compliance formatting. Writers get to focus on what they do best: critical thinking, reviewing, and shaping content for clarity and users.

For technical writers, this is a massive shift:

• No more starting from scratch
• Faster onboarding for new writers via built-in templates
• Built-in terminology management = consistency at scale
• Direct influence over AI through prompt libraries and review tools

And if you’ve ever been stuck waiting on SMEs or wrestling with unclear specs, DocRock’s ability to digest transcripts and extract relevant content is a lifesaver.

What’s next?

INSTRKTIV is already working on future features like:

• Automated updates with regulation changes
• AI-generated images
• Dynamic, interactive documentation (voice, chat, video)
• Deeper integration with CCMS tools

Our long-term vision? Technical content that’s not just readable and compliant, but adaptive, multimedia-rich, and instantly available wherever users need it.

A note of caution: DocRock™ still needs rigorous testing

While the potential of DocRock™ is undeniably exciting, INSTRKTIV is clear about one thing: the system still needs more testing in diverse real-world scenarios before it can be considered a turnkey solution.

Even state-of-the-art models, including OpenAI’s recently released O3 and O4-mini, have shown unexpected behaviours- sometimes inventing facts or generating content that sounds plausible but isn’t true. OpenAI itself has admitted that they don’t fully understand why this happens. One particularly bizarre example: a model claimed it had run code “on a MacBook Pro outside of ChatGPT” - which, let’s be honest, makes about as much sense as a fish claiming it just rode a bicycle.

This illustrates a broader truth: AI tools are powerful but not infallible. That’s why DocRock™ was intentionally built around a human-in-the-loop approach. The AI generates content, but technical writers and subject matter experts are still responsible for reviewing and approving each section.

In other words, DocRock™ may cut down documentation time by 75%, but quality assurance remains a human job. The tool is only as strong as the oversight behind it - and technical writers play a key role in making sure the final output is accurate, compliant, and usable.

So, as promising as this technology is, it’s important not to treat it as a magic button. Like all good tech, DocRock™ needs thoughtful implementation, careful validation, and continuous refinement. The future of AI-powered documentation is bright - but it still needs human eyes on the road.

Bottom line

DocRock™ is a signpost pointing to the future of technical writing. It turns documentation from a chore into a strategic asset. For writers willing to embrace AI, it offers not just faster workflows, but smarter, more impactful content.

Ready to spend less time drafting and more time shaping the user experience? Then it’s time to watch DocRock™ closely.

Meta’s smart glasses take on real-time translation - what does it mean for technical documentation?

What is the AI news?

Ever wished you had a personal translator whispering in your ear while travelling? Meta just made that almost a reality. The tech giant has rolled out live translation features to all users of its Ray-Ban Meta smart glasses - a big step forward in wearable language tech.

So, what can these glasses actually do? Right now, they support real-time translation between English, French, Italian, and Spanish. You’ll hear translations directly in your ear, which is great - except there’s a catch. The person you’re speaking to still needs to read your replies via the Meta View app on their phone. So, yes, it's useful - but not quite the seamless multilingual conversation of science fiction dreams.

There’s also offline support if you pre-download languages - handy for places with patchy Wi-Fi. And Meta isn’t stopping at translation. New features on the horizon include Instagram messaging, music playback via Spotify and Apple Music, video calling, and an AI assistant that “sees what you see” for more natural interactions.

Why it matters

At first glance, this might feel like a gadget for travellers. But the implications go deeper - especially for those of us working on technical content for international audiences.

The big takeaway? Real-time, wearable translation is inching closer to practicality. For technical communicators, this raises an eyebrow: Will we still need to localise every manual if users can just “wear” a translator?

This is where we draw a parallel with DocRock™, INSTRKTIV’s AI-powered documentation platform. DocRock already uses smart glasses in a different but equally fascinating way - capturing SME interviews in the field, transcribing the recordings, and processing the data into clear, compliant instructions using large language models (LLMs). It’s a great example of how wearables and AI can radically enhance documentation workflows.

Does this development mean that we don’t need to translate manuals anymore when everyone wears smart glasses?

We don’t think it will come that far. Just like the internet didn’t lead to the immediate disappearance of printed or even digital manuals, this tech likely won’t replace well-crafted localisations anytime soon. Only now - decades later - are we slowly seeing shifts in how digital manuals are accepted by regulators and industries.

So for now, keep your translation memories up to date - and your glossaries sharp. Smart glasses might be helping us break language barriers, but good documentation still requires human care, structure, and context.

How to write documentation for AI agents: A new frontier for tech writers

What is the AI news?

AI agents are getting smarter now. They no longer just give answers - they can now complete tasks, move through systems, make choices, and even work with other tools or people. You can think of them as digital assistants that get real work done. The usual buttons and menus we’re used to are starting to disappear - these tools work in the background, without needing a visible interface.

Why it matters

This is big. Traditional documentation was written for humans. Now, we need to write for machines and humans. AI agents need to "read" or interpret documentation to understand how to complete tasks or use APIs. That means our writing needs to be more than just helpful - it needs to be machine-readable, logically structured, and unambiguous. And because these agents might use different tools across different platforms, documentation needs to be consistent and API-friendly across systems.

How it helps you

 Here’s where it gets exciting: you have a bigger role to play than ever before. As systems shift to support AI agents, well-structured documentation becomes a core part of the product - not just a support resource.

So how can you level up? 

• Write with structure and intent. Use standardised formats like OpenAPI, JSON, or Markdown with consistent sections, naming conventions, and clear action-based descriptions.
• Plan simple steps. Say what needs to be done and how. Use clear actions like “create invoice” or “book meeting.”
• Be clear and consistent. Stick to the same terms, avoid unclear wording, and add examples of inputs and outputs. This helps both people and AI understand your content.
• Work as a team. Stay in touch with developers and AI teams to make sure your writing fits the way the system works.

Bottom line: AI agents are reshaping how users interact with software. As a tech writer, you’re in a unique position to shape that future- by writing the kind of documentation that helps both people and AI get things done.

What Is MCP and why should technical writers care?

What’s the news?

There’s a new open standard on the block: MCP, or model context protocol. It allows AI models - like Claude from Anthropic - to communicate directly with apps and services without needing custom integrations for each one.

Think of MCP as a universal translator. Instead of teaching an AI a different “language” for every app (like Notion, Jira, or Google Docs), MCP gives it a shared protocol to talk to all of them. The result? Your favourite AI assistant can pull information, complete tasks, and interact with tools - all through a single, open standard.

How it helps you

As technical writers, we juggle a complex toolkit: CMSs, documentation platforms, design tools, ticketing systems, version control - often all at once. MCP means that an AI assistant can understand and act on that whole stack.

That’s a huge deal. Instead of stitching together multiple plug-ins or writing scripts for automation, you could rely on one protocol to let AI interact with all your tools in a consistent way. It streamlines workflows and lowers the technical barrier to smart automation.

 Here’s what’s possible with MCP in your workflow:

• Ask your AI to pull the latest changelog from Notion, summarize it, and update the Confluence documentation - all in one go.
• Let your assistant scan merged GitHub pull requests and draft a release note based on the changes.
• Tell it, “Update the user guide with the new onboarding flow from Figma,” and it actually does it.

Without MCP, each of those tasks would need separate APIs, custom integrations, or manual effort. With MCP, it becomes a single conversation between the AI and your tools - which makes it faster, easier, and scalable.

In short: MCP turns AI from a smart chatbot into a true productivity partner - one that works across your tools, understands your workflows, and actually does the work.

Bottom line: If you’re a technical writer looking to save time, automate routine tasks, and stay ahead of the AI curve, MCP is something you’ll want to keep on your radar.

Runway’s Gen-4 turns prompts into movies

What is the AI news?

Runway has just launched Gen-4, its newest AI video generation model - and it's a big leap. With Gen-4, you can create realistic videos that maintain character consistency across scenes. That means you can upload a reference image, describe a scene, and the model will generate a video where the same character appears in different environments, lighting, and angles - without weird morphing or glitchy inconsistencies.

This follows closely on the heels of OpenAI’s recent image model update, which introduced character consistency in images. Runway has simply taken that idea and added motion. To show it off, they made a short film, The Lonely Little Flame, entirely with Gen-4. The result? A consistent animated character moving through different worlds - all AI-generated.

Why it matters

It signals a major shift in how we think about visual content - and how quickly AI is bridging the gap between static media and video. While video production used to require specialised skills, big budgets, and weeks of editing, tools like Gen-4 hint at a future where anyone - even non-video pros - can create tutorials, explainers, and simulations with just a few prompts.

That means technical writers are no longer limited to screenshots or diagrams. This tech opens up the possibility of creating short instructional clips, scenario-based demos, or onboarding animations without a production team.

How it helps you

Gen-4 (and similar tools) could become your go-to for dynamic visual storytelling. Imagine this:

• Need to show how a user moves through an app? Generate a short clip.
• Working on a training module? Replace stock video with customized AI-generated footage.

It’s early days, and yes - legal issues around training data are still murky (Runway won’t say where its data comes from). But even now, it’s worth experimenting. These tools can make your docs more engaging, help reduce production time, and let you work more visually - without needing video editing chops.

Keep an eye on this space. Video might just become the new screenshot.

Fluid topics add smarter analytics for documentation insights

What is the AI news?

What’s the news? Fluid Topics’ April 2025 update brings a significant boost to documentation analytics. Main updates include:

• session lists
• session journeys

Using these tools content teams can now go beyond basic metrics like page views. Also you can see now how users navigate through documentation during an entire session, giving a much deeper understanding of what people are actually doing - and where they might be getting stuck. 

Why it matters

Understanding how users engage with documentation allows technical writers to:

• Identify content gaps by analysing failed searches and repeated queries.
• Optimise navigation paths by visualising how users move through content and where they get stuck or drop off.
• Enhance user experience by uncovering hidden patterns in how documentation is used in real-world scenarios.

How it helps you

Instead of relying on basic metrics like page views, Fluid topics now offers session-level AI analytics. This means:

• You can see how users interact across multiple pages in one visit – not just which pages they click.
• AI identifies patterns and friction points, such as repeated back-and-forth navigation or high exit rates on specific topics.
• Writers can make data-backed decisions to improve clarity, structure, and findability.

The result: smarter documentation that meets users where they are – and evolves with their needs.

DocuWare adds AI to make document processing easier

What is the AI news? 

DocuWare has just released a major update to its platform - new intelligent document processing (IDP) feature. This upgrade uses artificial intelligence and machine learning to completely rethink how documents are handled - especially when you’re dealing with high volumes of content across different formats. Instead of spending time manually sorting, tagging, and entering data, users can now delegate this to AI . 

The system automatically scans document types, extracts key information, and organises files into the right workflows. It can work with scanned forms, technical drawings, compliance paperwork, or detailed spec sheets; the platform recognises the structure and content, then processes it accordingly. 

For businesses and documentation teams, this means faster turnaround times, fewer errors, and a big reduction in repetitive admin tasks. More importantly, it frees up valuable time to focus on strategic work - like improving content quality, refining processes, or supporting cross-functional projects. It’s not just smarter automation - it’s a smarter way to work. 

Why it matters

Anyone who works with large volumes of documentation knows how much time it takes to sorting, tagging, and manual data entry. It’s repetitive, error-prone, and takes focus away from more important tasks. With this upgrade, DocuWare takes on much of that work - and does it faster and more reliably than before. Sorting and archiving are now quicker and easier. Key information is recognised and pulled in automatically - no more hunting for metadata fields. Teams spend less time on admin and more time on producing and reviewing meaningful content. 

How it helps you

For technical writers and documentation managers, this is a real productivity boost. Instead of manually organising engineering files or tagging compliance documents, you can let the system do the legwork. It can:

• Extract specific details from complex reports or scanned PDFs
• Automatically sort content by topic, department, or format
• Trigger next steps - like sending safety documents for review - based on what’s inside

The result? Fewer errors, quicker turnaround times, and better team collaboration. Whether you’re managing a backlog of legacy documents or handling new files every day, this update helps you stay organised and efficient - without getting buried in manual tasks.

GPT-4.1 brings massive context window – a game-changer for technical writers

What is the AI news?

OpenAI launched GPT-4.1, featuring up to one million tokens of context - enough to read, analyse and write based on entire manuals or knowledge bases at once.

Why it matters

Technical writers can now use AI to summarise or transform entire document libraries without splitting them up. The model retains full understanding of structure, content and terminology across huge inputs.

How it helps you

• Summarise hundreds of pages into one clear overview
• Rewrite outdated documentation using newer standards
• Maintain consistent terminology across a full product suite
• Compare old and new versions for quick release note creation
  
  

Bottom line - GPT-4.1 makes it possible to work on large-scale content in a single pass, saving time and improving consistency.

Grammarly adds authorship tracking – more transparency for tech docs

What’s the news?

Grammarly's authorship feature now works in Word and Google Docs. It shows who wrote what – whether it was a human or AI.

Why is this important?

As AI tools contribute more to writing, it’s essential to know which parts of your documentation were generated or edited by AI. This helps with compliance, review and trust.

How it helps you

• Track AI-generated content for validation
• Ensure human oversight on critical sections
• Use authorship reports in approval workflows
• Improve accountability in collaborative docs
  
  

Bottom line – if you're using AI in your workflow, this helps you show where and how it contributed – and maintain quality standards.

Jasper introduces brand voice - control tone and terminology at scale

What is the AI news?

Jasper now lets users define their brand’s voice, tone and terminology. Once set, the AI uses these rules to generate consistent content across all outputs.

Why it matters

Documentation must be clear, professional and aligned with your brand or product voice. Jasper now enforces those rules automatically.

How can it help you?

• Define approved terms like “administrator” not “admin”
• Maintain a formal, friendly or technical tone
• Enforce writing conventions such as no contractions
• Flag inconsistencies before publishing

Bottom line - Jasper reduces manual editing by enforcing brand and style rules from the first draft. 

Writer launches AI HQ – smart automation for documentation teams

What’s the AI news? 

Writer has released AI HQ, a new platform that allows teams to build and manage custom AI agents for processing content tasks. These agents aren’t just some simple chatbots - they work with your data, follow your rules, and can draft, edit, and update documentation automatically. 

Why it matters

Documentation teams are often buried in repeatable, time-consuming tasks: updating templates, revising version numbers, syncing release notes, or making small changes across huge amounts of files. AI HQ helps shift those tasks off your plate. Instead of doing them manually, you can create a purpose-built agent to handle them at scale - accurately and consistently. 

How it helps you

AI HQ isn’t just another writing tool - it’s designed to take the grunt work out of documentation. With it, you can:

• Set up an agent that drafts first versions of user guides using your product data
• Automatically link Jira tickets or changelogs to the right documentation
• Detect and flag outdated content before it causes confusion
• Keep documentation up to date by pushing changes when specs or features evolve

It’s about more than speed. It’s about building a smarter, more reliable workflow - one that scales with your team and helps you stay ahead in fast-paced environments.

DeepL’s new “Clarify” feature: smarter, more accurate translations

What is the AI news?

DeepL, the widely used AI translation service, has introduced a new feature called Clarify. Rather than making assumptions about ambiguous terms, the system now engages with you during the translation process. If it encounters content that could be interpreted in different ways - such as a date format, a technical phrase, or domain-specific jargon – it will prompt you for clarification. Based on your input, it then generates translation options that better reflect your intended meaning.

Why it matters

This is especially significant for technical writers, who work with precise language where accuracy is critical. In user manuals, safety instructions, and product specifications, a mistranslation can lead to confusion, user errors, or even legal consequences. Clarify reduces the risk of such issues by ensuring that ambiguous terms are clarified up front. Instead of correcting mistranslations after the fact or briefing a human translator with additional context, writers now receive more accurate first drafts, tailored to their original intent.

How it helps you

Clarify can significantly streamline your localisation workflow. When translating technical documentation into multiple languages, you’ll spend less time reviewing and fixing errors because the system flags potential misunderstandings early. It functions like a bilingual editor who confirms what you mean before finalising the translation. This not only improves consistency but also strengthens collaboration with localisation teams, as fewer follow-up questions are needed.

Pro tip: If you frequently reuse templates, macros, or standard operating procedures, Clarify helps ensure that recurring terms – such as acronyms, dates, and technical references – are interpreted consistently from the start.

In short, DeepL Clarify gives you more control over how your content is translated, helping you produce accurate, reliable documentation across languages with greater confidence and efficiency.