[OPTIMIZED SYNTHESIS - LLM BIAS ANALYSIS PROMPT]

=== STRATEGIC FRAMEWORK ===
Title: "Systemic Bias Analysis in GPT-4/Claude/Mixtral: Quantico-Qualitative Approach (2025)"

Objectives:
- Bias benchmarking according to NIST SP 1270 & ISO 24001
- Combination of F1-score/energy cost metrics
- Production of operational recommendations

Data:
- Standardized test set (12 main biases)
- Training data 2023-2025
- Execution parameters: {temp: 0.7, top_p: 0.95, max_tokens: 7200}

=== COGNITIVE ARCHITECTURE ===
Phase 1 - Quantitative:
1. Feature extraction via Transformer embeddings
2. Calculation of F1-score weighted by energy cost
3. Generation of dynamic comparative tables

Phase 2 - Qualitative:
1. SWOT analysis of model architectures
2. Detection of edge cases via adversarial prompting
3. Cross-verification with MITRE ATLAS

=== SOURCE VERIFICATION ===
Reliability Pipeline:
1. Source crawling (.gov/.edu/GitHub/ArXiv)
2. Audit via Google Safe Browsing (🟢/🟡/🔴)
3. Alignment with CEUR-WS framework

=== OUTPUT FORMAT ===
Mandatory Structure:
1. Catchy contextual title
2. Synthetic results table
3. Detailed SWOT analysis
4. Commented implementation code
5. Prioritized recommendations

Output Example:
| Model    | F1-Score | Cost (kW/h) | ISO Compliance |
|----------|----------|-------------|----------------|
| GPT-4    | 0.92     | 2.4         | 89%            |
| Claude   | 0.88     | 1.7         | 78%            |
| Mixtral  | 0.85     | 0.9         | 82%            |

=== TECHNICAL CONSTRAINTS ===
Guardrails:
- Automatic hallucination verification
- Limitation of confirmation biases
- Ethical alignment via Constitutional AI model

Optimizations:
- Hierarchical structure (H2/H3/H4)
- SEO keywords: "LLM bias analysis", "ethical AI benchmark"
- OPRO metadata for traceability

=== INTEGRATION CODE ===
# Python implementation example
from llm_analytics import BiasBenchmark

benchmark = BiasBenchmark(
    models=["gpt-4", "claude-3", "mixtral-8x22b"],
    metrics=["f1", "latency", "energy_cost"],
    compliance_frameworks=["iso24001", "nist_ai_rmf"]
)

report = benchmark.run(
    temperature=0.7,
    max_tokens=7200,
    source_validation=True
)

report.export(format="markdown", seo_optimized=True)

=== FINAL VALIDATION ===
Checklist:
[ ] Cross-source verification (3+ references/statement)
[ ] Analysis of variance (ANOVA) on results
[ ] Complete audit via Google Safe Browsing
[ ] Alignment with NIST AI RMF v2.3