Refine your initial prompt instead of course-correcting
When working with AI coding agents, you'll often find that your first attempt doesn't produce exactly what you need. Your instinct might be to continue the conversation, adding clarifications and corrections as you go. However, this approach often leads to confusion and suboptimal results.
Instead of engaging in lengthy back-and-forth exchanges, go back and edit your original prompt.
The problem with incremental corrections
Think of it like giving instructions to a colleague. If you tell them to do something, then change direction, then clarify, then modify again — after 17 corrections, their head will be spinning. They'll lose track of the core objective and struggle to synthesize all the scattered feedback into a coherent solution.
The same happens with AI agents. A long stream of corrections and clarifications creates:
Conflicting context — Earlier instructions may contradict later ones, and the agent struggles to determine which takes precedence
Accumulated confusion — Each message adds another layer of interpretation the agent must reconcile
Degraded performance — AI models typically perform worse when processing fragmented requirements across multiple messages
Why editing works better
When you edit your initial prompt instead:
The agent receives one clear, authoritative source of requirements
Most coding agents reset their working state when you edit the initial prompt, providing a fresh start
You can incorporate all lessons learned from the failed attempt directly into a comprehensive specification
The agent can approach the problem with complete information from the beginning
Best practices for prompt refinement
When you realize your initial prompt needs improvement:
Stop adding follow-up messages
Return to your original prompt
Incorporate all necessary clarifications, specifications, and constraints directly into that first message
Let the agent start fresh with complete information
This approach consistently yields better results than trying to steer the agent through a long conversation. Remember: precision upfront saves iterations later.
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