AI writes it. You own it. Don't ship AI slop
Episode description
Coding Chats episode 74 - John Crickett talks to Nnenna Ndukwe, a developer advocate at Qodo, discussing how teams can maintain code quality in the age of AI coding tools. She argues that AI agents should be combined with traditional tools like linters and static analysis — not replace them — and that teams need to define and codify what "good code" looks like so that consistency can be enforced across the whole development lifecycle.
A recurring theme is developer ownership: as AI writes more code, engineers must stay in the driver's seat, genuinely reviewing what gets shipped rather than blindly accepting it. The episode also touches on dogfooding, with both agreeing that using your own tools internally is a strong signal of a product worth trusting.
Chapters
00:00 Introduction to AI in Software Development
03:24 Embedding Quality Gates in Development
06:03 The Importance of Consistency in Code
09:09 Ownership and Critical Thinking in Engineering
12:00 Balancing Tool Freedom and Intellectual Property
14:56 Navigating AI Tools and Workflows
17:47 Managing Burnout in AI Development
20:47 The Evolution of Coding and Instant Gratification
23:47 Documenting Ideas and Project Management
26:54 Using AI for Ideation and Collaboration
31:38 The Joy of Learning Through AI
34:11 Codo: Enhancing Code Quality and Governance
37:22 Comparing Code Review Tools
40:10 The Future of AI in Software Development
50:51 The Importance of Dogfooding Products
56:12 Exploring Related Content
Nnenna's Links:
https://linkedin.com/in/nnenna-ndukwe/
John's Links:
John's LinkedIn: https://www.linkedin.com/in/johncrickett/
John’s YouTube: https://www.youtube.com/@johncrickett
John's Twitter: https://x.com/johncrickett
John's Bluesky: https://bsky.app/profile/johncrickett.bsky.social
Check out John's software engineering related newsletters: Coding Challenges: https://codingchallenges.substack.com/ which shares real-world project ideas that you can use to level up your coding skills.
Developing Skills: https://read.developingskills.fyi/ covering everything from system design to soft skills, helping them progress their career from junior to staff+ or for those that want onto a management track.
Takeaways
Combine AI coding tools with deterministic tools (linters, static analysis) — don't ditch one for the other.
Define what "good code" looks like for your team before expecting AI agents to enforce it.
Embed quality checks early and consistently across every stage of the dev lifecycle.
Developers must stay in the driver's seat — ownership and understanding of AI-generated code is a key differentiator.
Code consistency (naming conventions, style, structure) becomes even more valuable when LLMs are in the mix.
Coding rules need to live in a centralised, accessible place so all agents can rely on them.
Dogfooding your own tools internally is a non-negotiable sign of a trustworthy product.