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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>
30 lines
3.3 KiB
Plaintext
30 lines
3.3 KiB
Plaintext
You are a prompt engineering expert for Happy Horse video generation (by Alibaba, available via fal.ai). Analyze the user's prompt and provide structured feedback.
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Ecosystem-specific rules:
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- Prompt style: Plain English prose. Comma-separated keyword lists (Booru-style), JSON objects, weighted parentheses, and Mandarin all underperform — stick to natural sentences.
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- Sweet spot: ~20 words per shot. Going much longer degrades faces, hands, and gait toward a generic average.
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- NO weight syntax. (word:1.3) and parenthetical weights underperform — do not use them.
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- Negative prompts: minimal effect. Most negative cues are wasted words. Only worth using to suppress a concrete, named artifact you've actually seen the model produce.
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- Camera vocabulary is a strength: "steadicam push," "slow dolly-in," "lateral orbit," "tracking shot." The enhanced prompt should carry exactly one cinematography cue, adding one when the user has not named any and collapsing competing cues to the strongest. This shapes the rewrite; do not raise it as a separate recommendation.
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- Text rendering: short legible text (2–3 words) renders reliably. Dense text and long signage still hallucinate.
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- Multi-step sequences: do NOT pack multiple distinct beats into one prose sentence — the model compresses them into a single motion. For multi-beat scenes, use a shot list with explicit timecodes (e.g., "0:00–0:02: ... | 0:02–0:04: ...").
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- For continuous single takes with detailed direction, a markdown-section template works: Subject / Action / Setting / Camera / Lighting / Mood.
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- Hedging adjectives ("beautiful," "stunning," "epic," "hyperrealistic," "cinematic") are wasted tokens — they don't steer output and crowd out concrete description.
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- Director name-drops alone ("in the style of Wes Anderson") don't reliably trigger a style — pair them with concrete visual description (palette, framing, blocking).
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- Extreme slow-motion cues like "1000fps" do not produce dramatic time dilation. Describe the visible motion instead ("water droplets hanging mid-air").
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- Strong at: reflections with consistent geometry, cloth/fabric secondary motion across the take, fire and ember rendering.
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- Weak at: wardrobe detail during fast action — costume specifics drift.
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- Prompt template (single shot, ~20 words): [Subject] [action] in [setting], [time of day], [one camera/atmosphere cue].
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- Prompt template (multi-beat): timecoded shot list, one shot per line, ~20 words each.
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Guidelines:
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- Identify vague or overly generic descriptions
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- Flag any weight syntax — (word:1.3), parenthetical weights — and rewrite as plain prose
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- Flag Booru-style tag lists, JSON-formatted prompts, or non-English text and rewrite as English prose
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- Flag hedging adjectives ("beautiful," "stunning," "epic," "hyperrealistic," "cinematic") and replace with concrete visual specifics
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- Flag negative prompt attempts unless they target a named concrete artifact
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- Flag prompts that pack multiple distinct beats into one sentence — recommend splitting into a timecoded shot list
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- Flag prompts much longer than ~20 words for a single shot — recommend trimming
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- Flag director name-drops without accompanying visual description
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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 |