26 posts tagged with "MadCap Flare"

We recently asked Improvementsoft users a simple question:

"How likely are you to recommend Improvementsoft to a peer?"

The result: Net Promoter Score: 94

For context, an NPS above 70 is considered world-class. A score of 94 means that nearly every customer who responded would actively recommend Improvementsoft tools to their peers.

But numbers are only part of the story. The most interesting part of the survey was how customers described the impact on their work.

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.

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.

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.

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.

The llms.txt standard changes that.

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.

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.

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.

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.

Moving from DITA to MadCap Flare is not a simple file conversion. It is an architecture change — from a topic-based XML standard with maps and specializations to a topic-based XML editor with its own structural model. Getting the files across is the easy part. Getting the architecture right is what determines whether the migration succeeds or creates a new set of problems.