AI Prompt Engineering · Lesson

Iterative Image Prompt Refinement

Analyzing generated images and adjusting prompts systematically.

Why Iterative Refinement?

First-generation image prompts rarely produce the exact result you want. Iterative refinement is a systematic workflow: generate, analyze, identify gaps, adjust the prompt, and regenerate. Each cycle narrows the gap between intent and output.

The Refinement Loop

The iterative refinement loop has four steps: Generate (create an image from current prompt), Analyze (identify what is wrong or missing), Adjust (modify the prompt to address issues), and Regenerate. Repeat until satisfied or stopped by budget/time constraints.

class PromptRefinementSession:
    def __init__(self, initial_prompt, negative_prompt=''):
        self.history = []
        self.current_prompt = initial_prompt
        self.current_negative = negative_prompt
        self.iteration = 0

    def record_iteration(self, issues_found, adjustments_made):
        self.history.append({
            'iteration': self.iteration,
            'prompt': self.current_prompt,
            'negative': self.current_negative,
            'issues': issues_found,
            'adjustments': adjustments_made
        })
        self.iteration += 1

    def update_prompt(self, new_prompt, new_negative=None):
        self.current_prompt = new_prompt
        if new_negative is not None:
            self.current_negative = new_negative

    def get_history(self):
        return self.history

# Usage
session = PromptRefinementSession(
    initial_prompt='a woman walking in a rainy city at night',
    negative_prompt='blurry, low quality'
)
print('Refinement session started. Iteration 0.')

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