Accelerating AI Development Success: Part 2

Read items from part 1.

Item 4: Embrace AI for understanding and documenting existing codebases

Legacy code represents both the greatest challenge and opportunity for AI acceleration. When faced with unfamiliar or poorly documented codebases, AI can serve as an interpreter between past intentions and present understanding.

In my team, developers have used AI to analyze Java services & libraries, Rust applications, the sf-ui-web monorepo, even the PHP monolith. The models ingested entire codebases and generated documentation that made implicit knowledge explicit. This transformed black boxes into transparent systems without the massive time investment traditionally required.

Use AI to generate living documentation that stays relatively in sync with the code as it evolves. While the machine can't fully understand architectural nuances, it excels at producing consistent documentation of functions, classes, and subsystems. This addresses the reality that code alone rarely speaks for itself with sufficient clarity.

Onboarding new developers becomes dramatically more efficient when AI-generated documentation provides an initial mental model. The new team member still needs to engage directly with the code, but starts with contextual understanding instead of bewilderment.

Item 5: Iterate and refine through AI-assisted experimentation

AI fundamentally changes the economics of experimentation. What once took days now takes minutes. Use this to your advantage by rapidly exploring alternative approaches instead of committing to a single path too early.

The upside-down engineering economics of software development - where experimentation costs less than complex up-front planning - becomes even more pronounced with AI assistance. Leverage this by prototyping UI walkthroughs, testing different integration patterns, or exploring alternative data structures with minimal investment.

My teams have used AI to generate guided filter searches and create UI walkthroughs with V0. These rapid experiments gathered feedback quickly, allowing teams to converge on effective solutions before committing significant development resources. The speed of prototyping enabled teams to learn through doing rather than speculating.

Make experimentation concrete by transforming vague ideas into working prototypes the team can evaluate. When stakeholders can interact with something tangible - even if flawed - their feedback becomes more useful than responses to abstract descriptions. AI accelerates this concretization journey from days to minutes.

Item 6: Maintain detailed developer notes on AI interactions and outcomes

Developer notes serve as memory in AI-accelerated projects. Track what the AI suggested, what you accepted or rejected, and why those decisions were made. This practice builds institutional knowledge that survives after the code is written.

Document both successes and failures in your AI interactions. When the machine produces an elegant solution, note the prompt structure and approach that led to success. When it generates flawed or misleading code, document the nature of the error to avoid similar pitfalls in the future.

These notes serve multiple purposes. They provide context for future developers maintaining the system. They accelerate your own learning about effective AI collaboration. And they create accountability for code provenance in systems where generation might otherwise obscure authorship.

Consider these notes part of your codebase rather than personal artifacts. When organized and accessible to the team, they become a knowledge base that compounds in value over time. Just as comments explain why code works a certain way, AI interaction notes explain why code exists in its current form.

(continued)