Building Brand Training Datasets
Python# Dataset structure for brand AI training brand_dataset = { "positive_examples": {"designs/", "metadata/positive_tags.json"}, "negative_examples": {"violations/", "metadata/negative_labels.json"}, "comparative_pairs": {"pairs/", "metadata/preferences.json"}, "process_docs": {"decisions/", "metadata/reasoning.json"} } # Annotation schema template annotation_schema = { "design_id": "BD_2024_001", "brand_alignment_score": 8.5, "style_tags": ["minimalist", "corporate", "tech"], "violation_type": null, "preference_rationale": "Better color harmony" }
Progress:
- Strategy 1: Collect positive brand examples
- Strategy 2: Gather negative violation examples
- Strategy 3: Create comparative A/B pairs
- Strategy 4: Document design process reasoning
Strategy 1: Positive Examples
Data Collection: Curate approved brand materials (logos, layouts, campaigns) Annotation Schema:
JSON{ "design_id": "string", "brand_elements": ["color_palette", "typography", "layout"], "alignment_score": 1-10, "style_tags": ["modern", "professional", "accessible"], "usage_context": "web|print|social" }
Quality Threshold: ≥8/10 brand alignment score, approved by brand manager Target Volume: 2,500 examples per quarter
Strategy 2: Negative Examples
Data Collection: Failed designs, competitor analysis, intentional violations Annotation Schema:
JSON{ "design_id": "string", "violation_types": ["wrong_colors", "off_brand_fonts", "poor_hierarchy"], "severity": "minor|major|critical", "correction_notes": "Use brand blue (#1234AB) instead", "learning_category": "color|typography|layout|voice" }
Quality Threshold: Clear violation identification, actionable feedback Target Volume: 1,500 examples per quarter
Strategy 3: Comparative Pairs
Data Collection: A/B test results, design iterations, preference studies Annotation Schema:
JSON{ "pair_id": "string", "option_a": "design_id_1", "option_b": "design_id_2", "preference": "a|b|neutral", "confidence": 1-5, "rationale": "Option A better reflects brand personality", "criteria": ["brand_fit", "usability", "aesthetic"] }
Quality Threshold: ≥3/5 confidence, clear rationale provided Target Volume: 1,000 pairs per quarter
Strategy 4: Process Documentation
Data Collection: Design reviews, decision logs, brand guideline applications Annotation Schema:
JSON{ "decision_id": "string", "design_stage": "concept|iteration|final", "decision_point": "color selection for CTA button", "options_considered": ["#FF6B35", "#1234AB", "#2ECC71"], "chosen_option": "#1234AB", "reasoning": "Aligns with primary brand color, ensures accessibility", "brand_principle": "consistency and accessibility" }
Quality Threshold: Complete reasoning chain, linked to brand guidelines Target Volume: 800 decisions per quarter
Example 1: Positive Annotation Input: Corporate website hero section Output:
JSON{ "design_id": "WEB_2024_045", "brand_alignment_score": 9.2, "style_tags": ["clean", "professional", "tech-forward"], "brand_elements": ["primary_blue", "montserrat_font", "grid_layout"], "usage_context": "web" }
Example 2: Comparative Pair Input: Two logo variations Output:
JSON{ "pair_id": "LOGO_COMP_012", "preference": "a", "confidence": 4, "rationale": "Version A maintains better legibility at small sizes while preserving brand character", "criteria": ["scalability", "brand_recognition", "technical_requirements"] }
- Maintain 70/20/10 split: 70% positive examples, 20% negative, 10% edge cases
- Version control datasets: Track changes and maintain lineage
- Cross-validate annotations: Multiple reviewers for subjective assessments
- Regular quality audits: Monthly review of annotation consistency
- Incremental updates: Add new examples as brand evolves
- Annotation drift: Reviewers becoming inconsistent over time
- Dataset bias: Over-representing certain design categories
- Insufficient negatives: Not enough clear violation examples
- Missing context: Failing to capture usage scenarios
- Static guidelines: Not updating as brand evolves
- Inter-annotator disagreement: Lack of clear scoring rubrics