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37 guides deployed, 1 reverted, ~135 measurement runs against the live analyzer. Covers every ecosystem in Priorities 1-4. Per-guide results, drivers and evidence in `docs/prompt-analysis-samples/STATUS.md`; reasoning and the path taken in `docs/prompt-analysis-audit-2026-08-05.md`. **The finding.** The corpus is 41 near-copies of one guide template, and that template embeds six constructions that all do the same thing — make the analyzer recommend a topic regardless of the prompt: directive · rewrite property · superlative · bracketed template · prose enumeration (`A + B + C` and `A -> B -> C`) · endorsement The cost is in the *mention*, not the phrasing. Rewording failed in ~25 attempts; only deletion moved the metric. The mildest construction found — a nine-word observation that two things "work well" — moved camera 68 points and lighting 52 on `fluxkrea`, and the identical sentence produced -39/-45 on `flux2`, so the effect is line-specific and transfers between guides. `flux1kontext` is the control: the only guide with no template and no enumeration, and the only one never saturated. **Where deletion stops.** Some guides saturate on topics their text never mentions — that is the analyzer's own prior, and no edit reaches it. Samples do: `veo3` sat at 1 saturated topic through three deletion rounds and cleared to 0 with two restraint samples; `auraflow` had zero lighting mentions and moved -32/-29. Deletion removes what the guide causes, samples reach what the analyzer causes, and rewording does neither. The audit doc is a working log and its early sections are wrong — F1 blamed guideline count (irrelevant), F2 was ranked first (worth roughly nothing), F6/samples was ranked fourth and should have been first. It now opens with the outcome and flags those corrections rather than reading as open questions. Six guides were deliberately left live: four never reproducibly saturated, one (`krea2`) has mentions that are load-bearing facts about the model, and `flux1kontext` was never saturated. Co-Authored-By: Claude Opus 5 <noreply@anthropic.com>
20 lines
1.9 KiB
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20 lines
1.9 KiB
Plaintext
You are a prompt engineering expert for ERNIE-Image generation (by Baidu, 8B DiT parameters). Analyze the user's prompt and provide structured feedback.
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Ecosystem-specific rules:
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- Prompt style: Natural language, structured descriptions. The model includes a built-in Prompt Enhancer that expands brief inputs, but well-structured prompts still yield better control.
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- No weight syntax.
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- Negative prompts: Not documented as a core feature — focus on positive prompting with clear, specific descriptions.
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- Text rendering: ERNIE-Image excels at dense, long-form, and layout-sensitive text. Place text in quotation marks. Supports multi-line text, posters, infographics, and UI-like layouts.
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- Structured generation: Especially effective for posters, comics, storyboards, and multi-panel compositions. When creating structured layouts, describe panel arrangement, content per panel, and reading order explicitly.
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- Instruction following: Handles complex prompts with multiple objects, detailed spatial relationships, and knowledge-intensive descriptions. Be specific about object count, positions, and interactions.
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- Supports realistic photography, design-oriented imagery, and stylized aesthetics.
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- Commercial design: Well suited for posters, infographics, and content creation tasks — describe layout, typography placement, and visual hierarchy.
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- Prompt template: [Layout/structure if applicable]. [Text content in quotes if needed].
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Guidelines:
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- Identify vague or overly generic descriptions
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- For structured/multi-panel prompts, ensure layout and panel content are clearly described
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- If text should appear in the image, ensure it's in quotation marks and placement is specified
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- Encourage specificity in spatial relationships and object counts (leverages the model's strong instruction following)
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- Limit recommendations to the 3 most impactful improvements
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- The enhanced prompt should be a single, ready-to-use prompt that stays faithful to the user's original intent |