AI Skill Report Card
Fine Tuning Brand Models
Fine-Tuning Brand Models
Quick Start13 / 15
Python# Brand Style Consistency Model - Priority 1 dataset_structure = { "brand_exemplars/": { "approved_copy/": ["headlines.json", "body_copy.json", "social.json"], "approved_visuals/": ["hero_images/", "product_shots/", "campaign_assets/"], "metadata.json": "brand attributes per asset" }, "negative_examples/": { "off_brand_copy/": "competitor/generic examples", "off_brand_visuals/": "style violations" }, "annotations/": "structured attribute tags" }
Recommendation▾
Add concrete input/output examples for visual model training, not just copy model examples
Workflow15 / 15
Phase 1: Dataset Curation Progress:
- Collect 500+ brand exemplars (approved assets)
- Source 200+ negative examples (off-brand content)
- Define annotation format with 4 core attributes
- Calculate volume requirements per model type
Phase 2: Data Annotation Progress:
- Tag composition (layout, hierarchy, spacing)
- Tag color mood (palette, saturation, contrast)
- Tag typography style (font personality, sizing, spacing)
- Tag target audience (demographics, psychographics)
Phase 3: Model Configuration Progress:
- Configure LoRA for copy model (LLaMA/Mistral)
- Configure LoRA for visual model (SDXL)
- Set training parameters and validation split
Phase 4: Evaluation & Iteration Progress:
- Implement brand consistency scoring
- Set up human preference ranking
- Create feedback loop for continuous improvement
Recommendation▾
Include specific brand consistency scoring algorithms or formulas rather than just mentioning the concept
Examples13 / 20
Example 1: Brand Exemplar Annotation Input: Approved headline "Crafted for the curious mind" Output:
JSON{ "text": "Crafted for the curious mind", "composition": "minimal_hierarchy", "color_mood": "sophisticated_neutral", "typography_style": "modern_serif_emphasis", "target_audience": "educated_professionals_25_45", "brand_score": 0.95 }
Example 2: LoRA Configuration for Copy Model Input: LLaMA-7B base model Output:
YAMLlora_config: r: 16 alpha: 32 dropout: 0.1 target_modules: ["q_proj", "v_proj", "o_proj"] dataset_size: 2000_examples epochs: 3 learning_rate: 2e-4
Recommendation▾
Provide actual code snippets for model inference/deployment, not just training configuration
Best Practices
Dataset Curation:
- Start with 500+ brand exemplars minimum
- Include 30% negative examples for contrast learning
- Balance content types (headlines, body, social, email)
- Version control all datasets with clear naming
Annotation Standards:
- Use consistent attribute vocabularies
- Score brand alignment 0-1 scale
- Include context metadata (campaign, channel, date)
- Multi-annotator agreement >0.8 for quality
Model Training:
- Use LoRA rank 16-32 for efficiency
- Train copy models on 2K+ examples
- Train visual models on 1K+ image pairs
- Validate on held-out brand campaigns
Evaluation Metrics:
- Brand Consistency Score: semantic similarity to exemplars
- Human Preference: A/B test branded vs generic outputs
- Coherence Score: cross-modal alignment (copy + visual)
- Business Impact: conversion/engagement lift
Common Pitfalls
Data Quality Issues:
- Don't mix different brand eras without version labels
- Don't include unapproved "almost there" examples
- Don't skip negative example collection
- Don't use generic stock content as positive examples
Training Mistakes:
- Don't overtrain on limited exemplars (causes overfitting)
- Don't ignore validation loss plateaus
- Don't use identical train/val splits across iterations
- Don't skip ablation studies on attribute importance
Evaluation Gaps:
- Don't rely solely on automated metrics
- Don't skip cross-channel consistency testing
- Don't ignore edge cases and brand boundary examples
- Don't deploy without human expert validation