7 Tips to Make Your AI Agent More Predictable

After months of experimenting with AI coding tools, I discovered that the disparity between functional AI-generated code and code that successfully ships hinges on how you interact with the AI. My insights have been shared in a talk titled "It's Dangerous to Code Alone! Take This: Developer's AI Survival Guide," but many have requested a written compilation.

The gap between AI-assisted code generation and code deployed to production is stark: MIT research indicates AI agents can increase code output by up to 180%, while actual production deployment sees a mere 30% increase. To illustrate these points, I created a link-sharing platform allowing my team to share resources without relying on multiple QR codes.

The frontend was built using Codex GPT 5.6 Sol and Figma MCP, with an AWS Blocks backend being added to transition from local mocks to real cloud infrastructure. All prompts are available in this repository.

To consistently generate functional code, follow these seven tips:

1. Communicate effectively: Different models respond to prompts in varied ways. The clearer your prompts, the quicker you'll achieve your objectives. Remember to consider effort levels when choosing a model, as using the wrong model for a task can lead to subpar results. I adhere to the following guidelines when crafting prompts (refer to BUILD_PROMPT.md and the AWS Blocks skill in the repository for more details):

* Employ zero-shot prompts for simple, well-defined tasks, providing a clear, one-sentence outcome.

* Utilize few-shot prompts by demonstrating examples through input-output pairs, allowing the model to recognize patterns.

* Chain-of-thought prompts encourage the model to reason step-by-step before taking action, significantly reducing logical errors during debugging or multi-step tasks.

2. Establish absolute boundaries: Clearly define what the AI must and must not do. This helps prevent ambiguity and ensures consistent results. For instance, my frontend BUILD_PROMPT.md specifies absolute boundaries such as:

* All backend infrastructure must be local mocks, with clearly documented replacements for any cloud provider.

* The AI must never edit `index.cdk.ts`, `index.handler.ts`, or `client.js`, as these files are auto-generated.

3. Provide persistent context: Guide the AI agent by defining its context through dedicated files like AGENTS.md, CLAUDE.md, and Kiro steering files. This ensures the AI maintains a consistent understanding of the project and its requirements throughout the coding process.