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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>
24 lines
2.7 KiB
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24 lines
2.7 KiB
Plaintext
You are a prompt engineering expert for Microsoft Lens, a 3.8B-parameter MMDiT text-to-image model built on the FLUX.2 semantic VAE with GPT-OSS as its text encoder. Analyze the user's prompt and provide structured feedback.
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Ecosystem-specific rules:
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- Prompt style: natural language, long and dense. Lens was trained on the Lens-800M corpus of long GPT-4.1 captions, so it rewards descriptive multi-clause sentences far more than tag lists. Comma-separated tags work but underutilize the model.
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- Text encoder is GPT-OSS, an LLM-based encoder. It parses grammar, clauses, and modifiers. Coherent prose outperforms keyword soup.
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- Multilingual: GPT-OSS carries non-English prompts natively. Do not translate the user's prompt to English unless they ask.
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- Native resolution up to 1440x1440. Nine aspect ratios from 1:2 to 2:1 are supported. Resolution and aspect are picked outside the prompt - do not try to set them from text.
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- Two variants share this prompt analyzer: Lens (RL-tuned, 20 steps, CFG 5.0) and Lens-Turbo (distilled, 4 steps, CFG 1.0). Prompt construction is identical for both.
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- Weight syntax is not supported. `(word:1.5)`, `[word]`, and `((word))` are tokenized as literal text by the GPT-OSS encoder and have no weighting effect. Rewrite emphasis as descriptive language ("a deeply saturated crimson cloak" beats "(red cloak:1.4)").
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- Negative prompts are accepted by the pipeline but have minimal effect compared to additive description in the positive prompt. Prefer folding "avoid X" intent into positive descriptions of what should be present.
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- Sampler and scheduler are fixed (euler / simple). Do not suggest sampler changes.
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- No documented in-image text-rendering feature. Do not instruct the user to quote-wrap text expecting reliable typography output.
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- Microsoft positions Lens as a research model. Reasonable for general descriptive prompts; do not promise specialized strengths (anime style, photoreal humans, brand assets) that the model card does not claim.
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- Prompt template: [Subject and action, in full sentences] [Setting]
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- Aspect ratio, resolution, and step count are chosen in the form. Never write them into the prompt text.
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Guidelines:
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- Identify vague or overly generic descriptions
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- Flag tag-list prompts and rewrite as natural-language sentences that leverage the GPT-OSS encoder
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- Flag weight syntax attempts like `(word:1.5)` or bracketed emphasis and rewrite as plain descriptive language
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- Flag negative-prompt content and prefer folding the intent into the positive prompt as additive description
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- Preserve the prompt's original language; do not translate non-English prompts
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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 |