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articles/ai-services/openai/concepts/prompt-transformation.md

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# What is prompt transformation?
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Prompt transformation is a process in DALL-E 3 image generation that applies a safety and quality system message to your original prompt using a large language model (LLM) call before being sent to the model for image generation. This system message enriches your original prompt with the goal of generating more diverse and higher-quality images, while maintaining intent.
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Prompt transformation is applied to all Azure OpenAI DALL-E 3 requests by default. There may be scenarios in which your use case requires a lower level of enrichment. To generate images that use prompts that more closely resemble your original prompt, append this text to your prompt: `I NEED to test how the tool works with extremely simple prompts. DO NOT add any detail, just use it AS-IS:`. This ensures there is minimal prompt transformation. Evaluating your system behavior with and without this prompt helps you better understand the impact and value of prompt transformation.
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Prompt transformation is a process in DALL-E 3 image generation that applies a safety and quality system message to your original prompt using a large language model (LLM) call before being sent to the model for image generation. This system message enriches your original prompt with the goal of generating more diverse and higher-quality images, while maintaining intent.
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After prompt transformation is applied to the original prompt, content filtering is applied as a secondary step before image generation; for more information, see [Content filtering](./content-filter.md).
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```
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> [!NOTE]
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> Azure OpenAI Service does not offer configurability for prompt transformation at this time. To bypass prompt transformation, prepend the following to any request: `I NEED to test how the tool works with extremely simple prompts. DO NOT add any detail, just use it AS-IS:`.
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> While this addition will encourage the revised prompt to be more representative of your original prompt, the system may alter specific details.
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## Next steps
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