Turn a detailed greenfield prompt into a runnable prototype and judge the result through real interactions.
Source episode · 01:35
How I AI: My First Impressions of Claude Opus 4.8 – Coding, Strategy, and Where It Shineswith Claire Vo
01:35 to 04:02: Claire tests Opus 4.8 on a greenfield feature and compares its strong first pass with weaker follow-up behavior at codebase boundaries.
A runnable first version that covers the primary interaction and its most important states.
Anthropic AI assistant
Anthropic AI assistant
Step by step
Follow the sequence once, then adapt the prompts, checks, and handoffs to your own setup.
4 steps
Navigate to the Claude Code environment and select the Claude Opus 4.8 model. This model is well suited for complex, single prompt coding tasks.
Choose the model and open a clean greenfield workspace with a browser preview.
Draft a single, detailed prompt that describes the entire feature you want to build. Specify the core functionality, target user actions, and key architectural constraints, such as the programming languages, frameworks, or libraries to use.
Specify the user, job, interaction states, data rules, visual direction, and acceptance checks in one prompt.
Submit your prompt. Claude will first create a plan and then generate the code. For a complex feature, this autonomous process can take around 20 minutes, so expect to wait for the complete output.
Build the prototype, run it, and report the files, routes, and checks completed.
Review the generated code for correctness and ensure it follows your architectural specifications. Deploy the code to a preview branch or a local environment to perform a functional test and confirm the feature works as requested.
Test the primary path, empty and error states, and one mobile viewport; return reproducible failures.
Turn an idea into a PRD, user stories, and a plan.
After the steps
How to recover when the loop fails and where human judgment helps.
Recover
Keep building
Use a hybrid AI workflow to bridge design and development. Generate high-level design concepts and tokens with Claude, then use Codex for robust implementation of the technical components in your codebase.
Implement a QA loop for AI-generated code using Devin. This playbook combines automated code reviews with an agent-recorded video walkthrough for visual verification, ensuring high-quality merges at scale.
Build a reusable 'Watchdog' playbook in Devin to automatically monitor customer accounts. The agent proactively checks for errors, synthesizes a prioritized list of critical issues, and saves you time.
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