The Challenge of Building a Project with AI

2026-06-20 · Source: AI on Medium · Field: Technology & Digital — Software Development & Engineering, Artificial Intelligence & Machine Learning · Depth: Novice, quick

Summary

This article details the author's experience building an AI project for a university, highlighting that the primary challenge was not coding but understanding real-world software product conception and development. The project emphasized strategic planning through Lean Inception, which involved defining a Minimum Viable Product (MVP) using INVEST criteria and translating user needs into User Stories to solve concrete problems. While AI code generation tools accelerated the initial development, the author found they often introduced errors requiring developer correction, underscoring that final logic remains human-controlled. The experience also covered the importance of frameworks, agile methodologies for adapting to rapid changes, and the demands of a production environment, including database persistence, robust security, automated testing, and scalable cloud deployment. Ultimately, the author concludes that while AI enhances speed, product design, user empathy, and human methodological rigor are the true driving forces.

Key takeaway

For AI Engineers or Software Engineers embarking on new AI projects, prioritize robust product design and agile methodologies over immediate coding. You should apply Lean Inception to define your Minimum Viable Product (MVP) and user stories before development, ensuring your solution addresses a concrete problem. While AI code generation tools can accelerate initial phases, meticulously review and correct their output, as human oversight remains critical for final logic and preventing errors in production environments.

Key insights

Building AI projects prioritizes human methodological rigor, product design, and user empathy over AI's speed advantages.

Principles

• Define a Minimum Viable Product (MVP) using INVEST criteria.
• AI code generation boosts starts but requires human logic control.
• Agile methodologies are crucial for adapting to rapid changes.

Method

Lean Inception involves strategic planning, scope reduction to define an MVP, and translating user needs into short User Stories under INVEST criteria before coding.

In practice

• Use Lean Inception to define MVP and User Stories.
• Integrate AI code generation tools for initial boosts.
• Implement robust security and automated testing for production.

Topics

• AI Project Management
• Lean Inception
• Minimum Viable Product
• AI Code Generation
• Agile Methodologies
• Software Production Environment

Best for: AI Student, Software Engineer, AI Engineer

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Editorial summary, takeaway, and curation by AIssential. Original article published by AI on Medium.