AI-assisted software engineering: challenges and opportunities

Coding Chats episode 57 - Owain Lewis and John Crickett explore the role of AI in software engineering, discussing the integration of AI into production systems and the challenges faced by AI engineers. They delve into the nuances of AI application, from building scalable systems to the importance of human oversight in AI-driven workflows.

Chapters

00:00 Defining the Role of AI Engineer

03:13 Exploring Large Language Models (LLMs)

06:15 Use Cases for LLMs in Business

09:22 The Non-Deterministic Nature of AI

12:12 AI in Software Engineering: The Future

15:11 The Role of AI in Code Review

17:53 The Bottleneck of Requirements Gathering

20:38 Leveraging AI Throughout the Software Lifecycle

29:53 Leveraging AI for Efficient Documentation

30:47 AI in API Design and Review

32:22 Spectrum Driven Development with AI

34:06 The Role of Requirements in Software Engineering

40:05 The Future of Programming Languages and AI

49:54 Understanding Context and Prompt Engineering

56:38 Exploring Related Content

Owain's Links:

Substack: https://newsletter.owainlewis.com/subscribe

AI Engineer: https://skool.com/aiengineer

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

AI engineering involves building software with AI, focusing on production systems.

AI engineers bridge the gap between AI model builders and software developers.

Understanding AI's unique paradigm is crucial for effective system architecture.

AI should be used minimally and where it makes sense, not everywhere.

Human oversight is essential in AI-driven workflows to ensure reliability.

Large language models (LLMs) are a significant focus in current AI trends.

AI can enhance business processes through automation and natural language interfaces.

AI's role in software engineering includes improving code quality and efficiency.

AI tools can amplify both good and bad engineering practices.

Experimentation and building real projects are key to learning AI engineering.