Raúl Berganza Gómez

Vibe coding sucks! Long life to vibe coding: Hardening Applications for Production with GenAI

'Vibe coding' with GenAI feels fast but creates massive tech debt. Learn to steer your AI toward robust, production-ready code.

Vibe coding sucks! Long life to vibe coding: Hardening Applications for Production with GenAI
#1about 4 minutes

The developer dilemma of adopting AI coding assistants

The developer community is split between techno-optimism and skepticism, but solid engineering fundamentals are essential to navigate the rise in technical debt from AI.

#2about 5 minutes

Keeping AI simple with break prompting and YOLO guards

Prevent AI from adding unsolicited features by separating planning from implementation and engineering friction into your workflow to maintain critical thinking.

#3about 2 minutes

Calibrating AI behavior using effective system prompts

Use system prompts to align the AI's behavior with your engineering preferences and enforce workflows, similar to onboarding a junior developer.

#4about 4 minutes

Managing high-level project context for AI agents

Overcome an AI's lack of high-level understanding by providing context upfront, such as business requirements, runtime specs, and project overviews.

#5about 2 minutes

Boosting productivity with reusable custom instructions

Save time and improve consistency by storing lengthy, detailed prompts for repetitive tasks as custom instructions that can be called with simple slash commands.

#6about 4 minutes

Using architectural anchors to guide complex AI tasks

Guide AI agents through complex implementations by first defining architectural anchors like data models, function signatures, and pseudo-code in comments.

#7about 2 minutes

The critical role of test-driven development with AI

Leverage test-driven development (TDD) to provide AI agents with a clear specification of behavior, but ensure you write the critical tests yourself to avoid spam coverage.

#8about 2 minutes

Writing secure code with a multi-pass development approach

Avoid "role stacking" by using an incremental, multi-pass approach where you first implement business logic, then separately address security and performance.

#9about 2 minutes

Empowering agents with custom and self-writing tools

Enhance AI capabilities by providing custom scripts as tools for specific tasks like performance profiling, or by prompting the agent to write its own tools on the fly.

#10about 2 minutes

Final thoughts on developer accountability and AI tooling

Remember to stack AI tools with classic deterministic scanners for security and performance, as the human engineer remains fully accountable for the final code.

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