Requirement to Release: How AI Supercharged Our Product Development Workflow

Author(s): Digvijay Mahapatra

Originally published on Towards AI.

One of the biggest problems my team(and probably yours) faces is the time it takes to build a product from start to finish. With the dawn of MCP, was it possible to improve efficiency and automate mundane tasks?

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Photo by Igor Omilaev on Unsplash

In today's tech landscape, introducing and making AI tools/agents an important part of product development has become crucial. AI presents unprecedented opportunities to enhance efficiency across the development lifecycle. Since its popularity, AI's biggest challenge has been sufficient context generation. It was easy but not simple. However, with the release of the Model Context Protocol, context generation proves less challenging every day.

Taking it upon myself as a challenge, I built a workflow that improves efficiency across my entire team (and hopefully yours), from gathering requirements to making a release. Integrating AI throughout our development pipeline reduces our end-to-end delivery time, with the most dramatic improvements in requirements management and test case creation.

Model Context Protocol (MCP) is a standardized way to programmatically provide context to large language models (LLMs) like ChatGPT or Claude. Its core advantage is its ability to eliminate manual work by enabling seamless API interactions and using responses as context for subsequent operations.

While not a comprehensive solution for all development challenges, MCP significantly accelerates workflows… Read the full blog for free on Medium.

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Published via Towards AI