Unlike models that immediately generate code, Opus 4.5 first created a detailed to-do list within the IDE. This planning phase resulted in a more thoughtful and functional redesign, demonstrating that a model's structured process is as crucial as its raw capability.
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To get superior results from AI coding agents, treat them like human developers by providing a detailed plan. Creating a Product Requirements Document (PRD) upfront leads to a more focused and accurate MVP, saving significant time on debugging and revisions later on.
Counterintuitively, a model optimized for writing (GPT 5.1 High) excels at the planning stage in Cursor's "plan mode" due to its strength in logical thinking and step-by-step reasoning. For the actual code execution, switch to a coding-specific model like Sonnet.
LLMs often get stuck or pursue incorrect paths on complex tasks. "Plan mode" forces Claude Code to present its step-by-step checklist for your approval before it starts editing files. This allows you to correct its logic and assumptions upfront, ensuring the final output aligns with your intent and saving time.
High productivity isn't about using AI for everything. It's a disciplined workflow: breaking a task into sub-problems, using an LLM for high-leverage parts like scaffolding and tests, and reserving human focus for the core implementation. This avoids the sunk cost of forcing AI on unsuitable tasks.
Unlike previous models that frequently failed, Opus 4.5 allows for a fluid, uninterrupted coding process. The AI can build complex applications from a simple prompt and autonomously fix its own errors, representing a significant leap in capability and reliability for developers.
Even for a simple personal project, starting with a Product Requirements Document (PRD) dramatically improves the output from AI code generation tools. Taking a few minutes to outline goals and features provides the necessary context for the AI to produce more accurate and relevant code, saving time on rework.
The comparison reveals that different AI models excel at specific tasks. Opus 4.5 is a strong front-end designer, while Codex 5.1 might be better for back-end logic. The optimal workflow involves "model switching"—assigning the right AI to the right part of the development process.
Unlike tools that immediately generate code from a prompt, Replit first engages in a planning phase. It collaborates with the user to define the structure and goals before execution. This structured, plan-first approach makes it a far stronger and more useful tool for product managers.
Borrowing from classic management theory, the most effective way to use AI agents is to fix problems at the earliest 'lowest value stage'. This means rigorously reviewing the agent's proposed plan *before* it writes any code, preventing costly rework later on.
To get AI agents to perform complex tasks in existing code, a three-stage workflow is key. First, have the agent research and objectively document how the codebase works. Second, use that research to create a step-by-step implementation plan. Finally, execute the plan. This structured approach prevents the agent from wasting context on discovery during implementation.
