Building iterative refinement loops

Iterative refinement loops create a systematic approach to improving AI outputs. Instead of random changes, you follow a structured process. Each loop brings you closer to the ideal result. This method turns good outputs into excellent ones consistently. The basic loop follows 4 steps:

• Analyze: Analyze the current output. What works and what needs improvement?
• Identify: Decide on the changes to request.
• Prompt: Write clear targeted instructions for the changes.
• Evaluate: Assess if changes improved the output.

Repeat until satisfied. Most outputs need 3 to 5 loops for optimization.