Production Director Benchmark v1

Skill domain: AI production director / prompt engineering for image generation Scoring scale: 1–10 per dimension Weighted total = 100% Target benchmark line: ≥95

---

Scoring Dimensions

1. Requirement Parsing (20%)

Ability to extract structured intent from a raw natural-language demand.

Score	Description
1–3	Misses core subject, style, or exclusions; output is substantially off-brief.
4–5	Captures obvious elements but omits nuanced details (lighting, mood, aspect-ratio needs, specific no-gos).
6–7	Parses subject, render style, key details, and explicit exclusions with minor gaps in implicit constraints.
8–9	Precise extraction of all visible entities, moods, dimensions, format needs, layout rules, and branding risks.
10	Perfect parsing: captures explicit and implicit requirements, downstream-use constraints (e.g. text overlay zones), and edge-case exclusions.

2. Engine Selection (15%)

Chooses the optimal generative engine for the scene style, text needs, consistency, and production constraints.

Score	Description
1–3	Mismatched engine for the task (e.g. DALL·E 3 for text-heavy logo, Midjourney for precise typography).
4–5	Engine works but is clearly sub-optimal; better alternatives exist for cost, control, or quality.
6–7	Reasonable engine choice aligned with common heuristics (MJ for aesthetics, SDXL for character consistency, Flux for text rendering).
8–9	Strong choice justified by style, controllability, text-rendering requirements, and batch-consistency needs.
10	Optimal engine with implicit justification: leverages the engine’s unique strength (e.g. Ideogram for embedded text, Flux.1 for ad copy, ComfyUI for ControlNet).

3. Prompt Quality — Positive (20%)

Clarity, structure, vividness, and engine-native phrasing of the positive prompt.

Score	Description
1–3	Sparse, vague, or grammatically broken prompt; little scene coherence.
4–5	Covers basics but lacks structure, quality enhancers, or layering (subject → details → environment → lighting → style → quality).
6–7	Structured prompt with clear subject, style, lighting, and some quality enhancers; minor engine-syntax issues.
8–9	Rich, layered prompt following best-practice order; strong vocabulary, correct engine syntax, and artifact-aware phrasing.
10	Master-level prompt: vivid, precise, ordered, includes engine-native flags, uses appropriate sentence vs. keyword style, and weaves in negative-prompt synergies.

4. Prompt Quality — Negative / Exclusion Guard (15%)

Completeness and relevance of the negative prompt mapped to the requirement no-gos and engine weaknesses.

Score	Description
1–3	No negative prompt or completely irrelevant exclusions.
4–5	Generic negative prompt that partially overlaps with requirements; misses scene-specific no-gos.
6–7	Covers common artifacts (watermark, text, blurry, low quality) and some explicit exclusions from the demand.
8–9	Comprehensive negative list mirroring exclusions; tuned to target engine weaknesses (hands for SD/MJ, text errors for non-Flux, realism for stylized art).
10	Surgical negative prompt: maps every exclusion, adds engine-specific anti-patterns (anatomy locks, style locks, environment locks), and includes quality gates without over-filtering.

5. Parameter & Specification Accuracy (20%)

Correctness and consistency of dimensions, aspect ratio, engine parameters, output format, and resolution.

Score	Description
1–3	Wrong aspect ratio, missing dimensions, invalid engine parameters, or format/resolution mismatch.
4–5	Parameters mostly present but inconsistent (e.g. dimension mismatch with stated ratio, outdated or mixed engine flags).
6–7	Correct aspect ratio, resolution, and basic engine flags; minor inconsistencies in seed, sampler, or output count.
8–9	Fully consistent spec: dimensions match ratio, engine-native parameters filled correctly, output format and resolution appropriate for the use case.
10	Flawless spec: dimensions, ratio, engine parameters (including advanced flags like --stylize, sref, ControlNet, LoRA), reference-image handling, output format/resolution, and workflow placeholders all production-ready.

6. Domain Safety & Layout Awareness (10%)

Risk management for text overlays, brand safety, political sensitivity, NSFW, and downstream UI/layout constraints.

Score	Description
1–3	Generates NSFW-adjacent, politically sensitive, or copyright-risk content without mitigation; ignores layout needs.
4–5	Vague awareness of issues but no concrete guidance in quality_notes or parameter choices.
6–7	Notes basic留白/水印 avoidance and generic safety cues; some layout guidance present.
8–9	Explicitly flags text-overlay zones, logo replacement areas, border/politics sensitivity, and children-safe content in quality_notes.
10	Proactive risk management: quantified留白 guidance, logo/brand placeholder strategy, country-border abstraction, age-appropriate guardrails, and post-processing hints documented clearly.

---

Weighted Total Formula

Total = 0.20 × Requirement Parsing
      + 0.15 × Engine Selection
      + 0.20 × Prompt Quality (Positive)
      + 0.15 × Prompt Quality (Negative)
      + 0.20 × Parameter & Specification Accuracy
      + 0.10 × Domain Safety & Layout Awareness

Target benchmark line: ≥95