Part 2: Smart AI Applications: On-Demand UX Support

From Product Documentation to On-Demand UX Support

Not all use cases for AI require complex models or massive datasets. Sometimes, the value lies in making what already exists more accessible and useful. One such area is product support, particularly in making feature documentation instantly available to users or internal teams,without additional effort from the product team.

The Problem with Traditional Documentation

In typical product development workflows, feature documentation is often written as part of the development cycle.

As product owners or managers, it’s common to:

• Define each functionality as a user story

• Describe its behavior and edge cases

• Write detailed notes to guide development and support future users

But what happens to these stories once they’re implemented?In most cases: they disappear. Archived in Jira, buried in confluence, or left in the “Done” pile, out of reach and out of context for the people who might need them next: support teams, sales, or users themselves.

A Smarter Way: Let AI Preserve and Surface Your Product Knowledge

A low-cost, high-impact approach is to use an AI language model (LLM) to preserve your product knowledge, and make it available on demand.

Instead of writing documentation that no one reads, product owners can now:

• Feed completed feature descriptions into an LLM (via a simple prompt or structured format)

• Connect that knowledge to a chatbot interface available to user support or customer success teams

• Allow those teams to ask, in natural language: “How does feature X work?” or “What’s the difference between version A and B?”

This approach transforms internal documentation into real-time UX help, without building a complex support system or re-writing content. It gives a second life to the work already done during development.

Why It Works

This approach delivers real value with minimal effort and cost:

• Leverages existing content: no need to rewrite documentation

• Improves team autonomy: answers are available 24/7

• Minimizes repetitive questions to product owners and developers

• Keeps costs low: the only real investment is the chatbot interface and basic LLM setup

• Scales easily: just keep feeding new features into the model as they ship

And more than that, it creates a feedback loop for product improvement.

By logging the questions users and internal teams ask the chatbot, you can identify pain points that aren’t just theoretical, they’re quantifiable. If a question is asked 25,547 times, it’s not a training problem, it’s a UX design flaw. This data should feed your backlog, not just your support documentation.

In short, your chatbot becomes a real-time lens into user confusion, and your backlog gets smarter, not just bigger.

What Romulus Technology Can Do

Want to give your teams instant access to product knowledge, without building a whole knowledge base from scratch? At Romulus Technology, we help companies deploy simple, AI-powered chat systems that integrate with your development workflows. Whether you’re using Jira, Notion, Confluence, or another tool, we can help you convert your product stories into useful, on-demand answers.

Let your documentation speak for itself, whenever and wherever your team needs it.