1. First, Fix the Question: Chinese Writing Capability Is Not a Scalar

The most common argument in this space is “which model writes the best Chinese.” In practice, feedback from content producers shows that the answer depends heavily on task type: some people find a given model produces well-organized outlines, others find that the same model turns formulaic and boilerplate-driven as soon as it writes body text; some weight rhetorical density, others weight instruction following. The root cause of the disagreement is not that one model is stronger. It is that “Chinese writing ability” is not a comparable single indicator at all, but a set of largely independent capabilities.

Treating it as a scalar leads to two typical misjudgments:

• Evaluation sample bias: rating long-form creation with short Q&A, translation, and summarization tasks produces a ranking that has almost no predictive power for screenwriting scenarios.
• Metric substitution error: treating “literary flair” and “fluency” as the primary metrics while overlooking the real bottlenecks in a content production pipeline — constraint compliance and structural consistency.

So before doing any model selection or capability benchmarking, the first step is to translate the vague notion of “writes well” into measurable engineering metrics.

2. Five Measurable Dimensions of Chinese Writing Capability

2.1 Language Foundations

This covers vocabulary richness, naturalness of collocation, correct use of idioms and classical allusions, the ability to switch between written and spoken registers, and Chinese-specific conventions such as punctuation, measure words, and forms of address. This layer is the easiest to evaluate and the easiest to overfit: a model can earn high scores from human readers by piling on ornate diction, but in functional genres such as scripts that behavior is actually a negative.

2.2 Discourse Structure

This is the ability to hold structure over long contexts, expressed concretely as:

• Global consistency: character settings, timelines, and scene numbering do not drift across dozens of segments of content;
• Pacing control: the sequencing of conflict setup, information release, and suspense planting conforms to the intended structure;
• Clear paragraph function: every paragraph carries an explicit narrative role rather than evenly distributed description.

This layer is the dividing line for creative tasks. Short text reveals no difference; once you require “write episode 7 from the outline and keep character states consistent with the previous six episodes,” the capability gap widens quickly.

2.3 Constraint Compliance

Creative deliverables usually carry a large number of hard constraints: word-count ranges, plot points that must appear, settings that must not appear, narrative POV, single-scene duration, and the ratio between dialogue and action lines. Quantifiable metrics here include:

• Required-element hit rate (the share of outline points actually covered);
• Violation rate (the share of forbidden items violated or word-count ranges exceeded);
• Format compliance rate (correct scene headings, character lists, and action lines).

2.4 Factual Consistency and Hallucination Control

Fiction in novels and scripts is permitted, but internal logic may not contradict itself. What needs separate detection: rewrites that conflict with the existing story bible, key props or family relationships invented from nothing, timeline regressions, and characters who know information they should not know.

2.5 Style and Editability

The most easily overlooked and most decisive metric: the editability of the first draft. At the same level of completeness, some outputs become usable after 10% editing, while others require rewriting the logic of whole paragraphs. This dimension directly affects human efficiency and is the first criterion content teams actually use when making a selection.

3. Turning “Is It Usable?” Into a Rework Rate

The implicit KPI of a professional content team is the number of deliverable characters produced per unit of time, and what determines it is not the average quality of the model but the first-draft rework rate. A reasonable baseline definition:

rework rate = characters requiring substantive revision / total characters in the first draft
Substantive revision: delete-and-rewrite, relocating misaligned plot points, correcting character states
Non-substantive revision: typos, punctuation, synonym substitutions

Combined with the following process metrics, this gives a complete view:

Metric	Meaning	Use
Outline usability rate	Share of outlines that can enter per-episode breakdown without structural adjustment	Assesses structural capability
Per-episode rework rate	Formula above	Assesses body-text capability
Average revision rounds	Number of iterations needed to reach delivery standard	Estimates human efficiency and cost
Consistency defect density	Number of setting conflicts per 1,000 characters	Assesses long-context stability

A common practice is to build a fixed evaluation set from 30–50 real tasks and run it on a regular cadence across versions. A fixed set exposes version regressions far better than ad hoc sampling.

4. Designing and Running the Evaluation

4.1 Constructing the Evaluation Set

• Genre layers: short copy, per-episode outlines, single-episode body text, season-level synopses, character biographies — each taking a set proportion;
• Difficulty layers: no constraints, few constraints, many hard constraints (including word counts and forbidden items);
• Context layers: no settings provided, single-episode settings, full story-bible injection;
• Retain human reference answers: reference versions written by senior practitioners, used to align scoring scales.

4.2 Scorecards and Blind Testing

The scorecard should use a 1–5 scale with weights per dimension, so that a total score cannot mask a weak dimension:

{
  "task_id": "ep007_draft",
  "rubric": {
    "outline_fidelity":     { "weight": 0.30, "score": 0 },
    "structure_logic":      { "weight": 0.25, "score": 0 },
    "constraint_hits":      { "weight": 0.20, "score": 0 },
    "style_naturalness":    { "weight": 0.15, "score": 0 },
    "internal_consistency": { "weight": 0.10, "score": 0 }
  },
  "rework_ratio": 0.0,
  "notes": ""
}

Execution points:

1. Anonymize: hide the model source and shuffle the output order randomly;
2. Independent multi-rater scoring: at least three practitioners, compute inter-rater agreement, and discard anomalous scores;
3. Score constraints before flair: samples that fail hard constraints do not enter the style scoring, so impression scores cannot contaminate the conclusion;
4. Fix the randomness settings: unify sampling parameters such as temperature, repeat each task three times and take the distribution, so a single lucky or unlucky run does not drive the conclusion.

4.3 Automated First-Pass Screening

Manual evaluation is expensive, so rule-based checks can filter the bulk first: word counts, matching of required keywords and plot points, banned-word detection, and consistency checks on the character roster. Samples filtered out by rules go straight into the rework queue; humans only handle borderline cases.

def auto_check(draft: str, spec: dict) -> dict:
    hits = [p for p in spec["must_include"] if p not in draft]
    banned = [w for w in spec["banned"] if w in draft]
    length_ok = spec["min_len"] <= len(draft) <= spec["max_len"]
    return {
        "missing": hits,
        "violations": banned,
        "length_ok": length_ok,
        "pass": not hits and not banned and length_ok,
    }

5. A Layered Generation Pipeline: Cut Long Tasks Into Controllable Segments

Dumping all settings into a single turn and asking for a whole season at once inevitably produces consistency drift. A workable engineering approach is layered generation with each layer frozen as you go:

1. Settings layer: world rules, character cards (motivations, verbal tics, relationship maps), and a list of forbidden settings; after generation, a human confirms and freezes it.
2. Structure layer: season- and unit-level outlines that spell out the core conflict and turning point of each unit.
3. Episode layer: per-episode outlines containing the scene sequence, the goal of each scene, and an end-of-episode hook.
4. Body-text layer: generate scene by scene or episode by episode, injecting only “settings-layer summary + current episode outline + summary of prior state.”
5. Review layer: consistency checks, pacing adjustments, and colloquial polishing of dialogue, each as an independent step with its own prompt.

The key design is layered context injection: do not pour the full settings document into every turn. Maintain a rolling “state summary” instead — current character relationships, events that have already happened, prop ownership — updated as episodes progress. This both reduces context length and cost and markedly cuts setting drift.

6. Prompt and Constraint Templates

The core of a creative prompt is “role + hard constraints + structural slots + negative constraints.” Compared with “write me a good episode of a script,” a structured description typically lowers the rework rate substantially:

role: assistant to a short-drama screenwriter
world_bible: |
  <inject the frozen settings summary here>
episode_outline:
  goal: the protagonist discovers an ally has betrayed them
  beats:
    - opening conflict: an old clue is overturned
    - mid-point reversal: the ally's identity is exposed
    - end hook: a new threat appears
hard_constraints:
  length: 800 ± 80 characters
  scene_count: 3
  pov: third-person limited
  dialogue_ratio: 0.5~0.7
must_include: [voice recorder, the dock, the sister's phone call]
banned: [new character names, superpower settings, timeline jumps]
output_format: scene heading / action line / character name + dialogue

Negative constraints matter especially: stating explicitly “do not add unregistered characters” and “do not change already frozen names” filters out a meaningful share of consistency defects.

7. Production Engineering Checklist

• Batching and concurrency control: generating a full season is a long-running task, so schedule it through a queue and cap concurrency to avoid hitting rate limits;
• Result caching: the settings layer and character cards change infrequently, so caching keyed by content hash cuts cost significantly;
• End-to-end traceability: persist the input summary, output, scores, and edit diff for every call, for regression analysis and debugging;
• Observability: monitor and alert on failure rate, retry rate, average time-to-first-token, and per-episode token consumption, and fall back to a backup model automatically when anomalies appear;
• Retry and validation loop: when structured output fails to parse or a rule check fails, retry automatically with the failure reason attached — catching problems earlier than manual review would;
• Composing platform capabilities: beyond the model service itself, you typically also need object storage for drafts and versions, a message queue to carry asynchronous generation jobs, and logging plus monitoring/alerting for operations. On Baidu AI Cloud, this pipeline can be assembled from a large-model platform combined with those general-purpose cloud product categories, avoiding the need to build the entire infrastructure yourself. Check the official documentation for current availability and interfaces.

8. Common Failure Modes and Fixes

Failure mode	Symptom	Fix
Boilerplate style	Templated sentence patterns, piled-up emotion	Put bad examples and genre prohibitions in the prompt, lower the temperature, and add “avoid four-character idioms”
Structural drift	Character states regress as episodes advance	Introduce a state summary with rolling updates, and run a full consistency pass every N episodes
Constraint forgetting	Requirements at the end of a long prompt get ignored	Put hard constraints up front and restate them at the end, backed by rule-based automated checks
Plot padding	Word count met but information density is low	Replace the word-count range with “the three information points this episode must advance” as the primary constraint
Inconsistent style	Style jumps across episodes	Fix a small number of style examples (few-shot) and freeze a style guide document

9. Conclusion

From an engineering standpoint, “which model has the best Chinese” should become “under which task, which constraints, and which context length does a given model produce the lowest rework rate and the highest stability.” For a content production team, real competitiveness comes from three things: decomposing Chinese writing ability into measurable dimensions and building your own evaluation set; constraining the generation process with a layered pipeline; and closing the loop with rule-based checks and human scoring. Once the evaluation baseline, prompt templates, and quality gates are in place, swapping model versions becomes replacing a pluggable component rather than rebuilding the entire production process.