Grounding AI in Reality: How Vector Search on Our Codebase Transformed Our SDLC Automation Antony Brahin 6 min read · 1 day ago 1 day ago -- Listen Share
By: Antony Brahin
In software development, the process of turning a user story into detailed documentation and actionable tasks is critical for success. However, this manual process can often be a source of inconsistency and a significant time investment. I was driven to see if I could streamline and elevate it.
The journey from a user story to a set of well-defined, actionable tasks is critical. It’s also often one of the most time-consuming, repetitive, and inconsistent parts of our workflow. That administrative grind isn’t just tedious; it’s where inconsistency creeps in and valuable time is lost. I was convinced we could automate it.
In this post, I’ll walk you through how I built a complete, end-to-end automation that takes a user story in Azure DevOps (ADO) and, using a sophisticated chain of AI prompts with Google’s Gemini and a vector search of our codebase, outputs a full requirements document, a technical specification, a test plan, and a complete set of ready-to-work tasks.
Why Build This When Commercial Tools Exist?
I know this is a hot space. Big players like GitHub and Atlassian are building integrated AI, and startups are offering specialized platforms. My goal wasn’t to compete with them, but to see what was possible by building a custom, “glass box” solution using the best tools for each part of the job, without being locked into a single ecosystem.
What makes this approach different is the flexibility and full control. Instead of a pre-packaged product, this is a resilient workflow built on Power Automate, which acts as the orchestrator for a sequence of API calls to multiple platforms. This allowed me to fine-tune every step of the process to our exact needs.
The Architecture: A High-Level View
The entire solution is a Power Automate cloud flow that orchestrates a series of API calls. It’s triggered by an ADO user story update and uses a combination of Gemini AI for generation, Retrieval-Augmented Generation (RAG) for code context, and direct ADO API calls for execution.
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