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What you are probably seeing
You review the generated charts and diagrams and notice subtle discrepancies where the data labels do not align perfectly with the source values.
The visual style mimics your brand guidelines loosely but introduces unexpected elements that create cognitive noise for the end user.
Why it happens
Large language models are trained on vast datasets and can hallucinate details when specific factual constraints are not strictly enforced in the prompt.
Manual design allows for precise control over negative space and typographic hierarchy that automated generation often compromises to fit the output.
What changed recently
Step by step
1. Define a written brand checklist including exact color codes and font weights before generating any visual assets.
2. Cross-reference every data point in the visualization against your raw spreadsheet to catch calculation errors.
3. Test the image at small screen sizes to ensure text remains legible without manual resizing.
4. Run a blind test with two colleagues to verify that the intended message is clear without explanation.
5. Keep the original source files and version history separate from the generated images for future audits.
Ways to go further
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