Negative prompting specifies what NOT to generate, helping avoid unwanted elements in image generation. How it works: Classifier-free guidance steers away from negative concepts. During generation, model moves toward positive prompt AND away from negative prompt. Common negatives: "blurry, low quality, bad anatomy, extra limbs, watermark, text, ugly, deformed, disfigured, out of frame, cropping". Use cases: Fix recurring issues (bad hands, extra fingers), avoid styles, remove artifacts, improve quality. Implementation: Negative embeddings computed, combined with unconditional and positive during CFG sampling. Per-model negatives: Different models have different failure modes, community-developed negative prompts per checkpoint. Negative embeddings: Textual inversions trained on bad outputs, e.g. "EasyNegative", "bad-hands-5". Best practices: Start with standard quality negatives, add specific negatives for observed problems, avoid over-negating (can distort output). Tools: All major diffusion UIs support negative prompts, AUTOMATIC1111, ComfyUI, InvokeAI. Essential technique for quality control.