Customer Survey Results: NPS 94 and Real Productivity Gains for Technical Writers
We recently asked Improvementsoft users a simple question:
What "AI-Ready Documentation" Actually Means — And How to Get There
Everyone says documentation should be "AI-ready." Few can explain what that means in practice. It is not a marketing label. It is a set of measurable structural properties that determine whether AI tools can use your content or will hallucinate around it.
MadCap Flare Conversion: How to Turn a Migration Into an Architecture Upgrade
A MadCap Flare conversion is usually treated as a logistics problem — move content from the old tool to the new one, preserve formatting, hit the deadline. That approach guarantees you will carry every structural problem from the old system into the new one. The migration is your best opportunity to fix what was always broken. Here is how to use it.
RAG Pipelines and Documentation: Why the Content Layer Is Your Biggest Risk
Your RAG pipeline is only as good as the content it retrieves. Teams spend months tuning embeddings, chunking strategies, and prompt templates — then feed the system documentation that was never designed for machine consumption. The result is confident, well-formatted answers built on garbage retrieval. The content layer is where most RAG implementations silently fail.
Making Your Documentation AI-Ready with llms.txt
AI tools are changing how people find and consume documentation. But most documentation systems weren't built for AI consumption — they were built for browsers. The result: AI models struggle to extract structured knowledge from your help output, and your users get incomplete or hallucinated answers.
Quality Drift in Documentation: How It Starts, How It Compounds, How to Stop It
Nobody ships a documentation project with the intention of letting quality degrade. It happens anyway. Not in a single event, but through hundreds of small decisions — a shortcut here, an exception there, a new writer who follows the patterns they see instead of the patterns you intended. This is quality drift, and by the time it becomes visible, the cost of fixing it has multiplied.
The Hidden Cost of a Bad Flare Migration — And How to Audit Yours
Most Flare migrations technically succeed. The content moves from the old system to the new one. Builds run. Outputs generate. Everyone declares victory. But six months later, writers are slower than before, builds take twice as long as they should, and nobody trusts the conditions. The migration didn't fail — it just deferred every structural decision to the future, and the future has arrived.
Before You Buy Another Documentation Tool: Run This Audit First
Documentation teams buy tools to solve problems. But most documentation problems are not tool problems — they are structural problems, process problems, or architecture problems that follow you from one tool to the next. Buying a new tool before diagnosing the actual bottleneck is the most expensive mistake in technical documentation.
How to Use AI With MadCap Flare Without Destroying Your Content Structure
AI can cut your drafting time in half. It can also silently destroy the structural integrity of your Flare project in a single paste operation. The difference is not whether you use AI — it is how. Most technical writers are using AI in a way that creates more cleanup work than it saves.