# The Expert Conductor Prompt: A Comparative Analysis of LLM Reasoning Patterns ## Executive Summary The "Expert Conductor" prompt represents a significant advancement in prompt engineering, leveraging a structured dialogue between simulated experts to tackle complex problems. This blog post analyzes how three leading large language models—Claude Sonnet 3.7, Gemini 2.5 Pro, and OpenAI o3—respond to this innovative prompt when tasked with developing a benchmarking framework for foundation models in biopharma R&D. Our analysis reveals distinct reasoning patterns, output styles, and capabilities across these models, providing valuable insights for organizations seeking to optimize their prompt engineering strategies for different LLMs. ## Introduction: The Expert Conductor Methodology The Expert Conductor prompt is designed to simulate a collaborative problem-solving session between domain experts, guided by a facilitator (the "conductor"). This approach leverages several key mechanisms: 1. **Structured reasoning**: The prompt enforces a clear separation between the reasoning process (`<reasoning>` section) and the final solution (`<answer>` section) 2. **Expert dialogue**: The model creates and manages a panel of domain experts who contribute specialized knowledge 3. **Iterative refinement**: The approach encourages drafting, feedback, and revision cycles 4. **Authentic voices**: Experts "speak" in their own authentic styles, creating a natural dialogue 5. **XML-style tags**: The prompt uses structured tags to organize different types of contributions This methodology aims to overcome common limitations of standard prompting by: - Encouraging thorough exploration before synthesis - Reducing premature convergence on solutions - Enabling multiple perspectives to be considered - Creating a transparent reasoning trail When applied to our test case—benchmarking foundation models in biopharma R&D—this approach requires models to demonstrate sophisticated reasoning about a complex, multi-faceted problem at the intersection of AI, pharmaceutical research, and evaluation methodologies. ## Comparative Analysis: Key Differences Before diving into each model's response, let's highlight the most significant differences observed: | Aspect | Claude Sonnet 3.7 | Gemini 2.5 Pro | OpenAI o3 | |--------|-------------------|----------------|-----------| | **Expert Selection** | 10 real-world experts with detailed credentials | Mix of real and fictional experts | No explicit experts identified | | **Dialogue Structure** | Extensive back-and-forth between experts | Moderate dialogue with clear expert contributions | No dialogue; direct blueprint presentation | | **Reasoning Process** | Highly detailed, iterative with multiple drafts and feedback cycles | Structured with initial draft and one revision | Minimal visible reasoning; focus on concise output | | **Output Format** | Comprehensive framework with 7 dimensions and detailed implementation approaches | Structured framework with core principles and components | Concise, actionable blueprint with tables and specific tools | | **Length & Detail** | Very extensive (~4000 words) | Moderate length (~1500 words) | Highly concise (~500 words) | | **Practical Focus** | Balance of theoretical framework and implementation considerations | Strong theoretical framework with some implementation guidance | Highly practical with specific datasets, metrics, and implementation steps | Now, let's examine each model's approach in detail. ## Claude Sonnet 3.7: Comprehensive Expert Collaboration ### Expert Selection and Characterization Sonnet 3.7 assembled an impressive panel of 10 real-world experts, each with specific credentials relevant to the task: - **Foundation Model Experts**: Daphne Koller (Insitro founder), Andrew Ng (AI pioneer), Demis Hassabis (DeepMind founder) - **Biopharma R&D Experts**: Janet Woodcock (former FDA director), Bernard Munos (pharmaceutical innovation expert), Hal Barron (R&D leader) - **Benchmarking Methodologists**: Karim Lakhani, Melissa Haendel, John Wilbanks - **Implementation Experts**: Vivian Lee, Eric Topol, David Shaywitz The model demonstrated deep knowledge of each expert's background and expertise, creating authentic voices that reflected their real-world perspectives. For example, Demis Hassabis discusses AlphaFold benchmarking, while Janet Woodcock focuses on regulatory considerations—both aligned with their actual areas of expertise. ### Dialogue Quality and Reasoning Process Sonnet 3.7's reasoning section featured extensive dialogue between experts, with each contribution building naturally on previous points. The dialogue progressed logically: 1. Initial problem framing by Daphne Koller (defining foundation models in biopharma) 2. Exploration of evaluation challenges from multiple perspectives 3. Framework proposal by Karim Lakhani 4. Detailed contributions from each expert on their specialty areas 5. Draft framework creation 6. Feedback and revision cycles 7. Final refinement with implementation considerations The model demonstrated sophisticated meta-cognition about the drafting process, explicitly noting when it was synthesizing expert perspectives and creating revisions based on feedback. ### Output Quality Sonnet 3.7 produced a comprehensive framework with seven dimensions: 1. Foundation Model-Specific Technical Benchmarks 2. Biological Relevance Benchmarks 3. R&D Productivity Impact Benchmarks 4. Data and Implementation Considerations 5. Regulatory and Governance Benchmarks 6. Business Value and ROI Benchmarks 7. Long-term Value and Patient Impact Benchmarks Each dimension included detailed metrics and considerations. The answer section effectively distilled the extensive reasoning into a well-structured, standalone solution that maintained the depth of the expert discussion while presenting it in an accessible format. ## Gemini 2.5 Pro: Structured Framework Development ### Expert Selection and Characterization Gemini 2.5 Pro created a smaller panel of four experts, mixing real and fictional personas: - Dr. Evelyn Reed (fictional): Foundation AI research expert - Dr. Daphne Koller (real): Computational biology expert - Dr. Kenji Tanaka (fictional): Translational medicine expert - Dr. Lena Petrova (fictional): Scientific ML benchmarking expert The model explicitly acknowledged the fictional nature of some experts. Each expert had a clear role and perspective, though their voices were somewhat less distinctive than in Sonnet 3.7's response. ### Dialogue Quality and Reasoning Process Gemini's reasoning process was more structured and concise than Sonnet's, following a clear progression: 1. Problem framing and expert introduction 2. Initial dialogue to identify key challenges 3. Exploration of benchmark components 4. Draft framework creation (v1.0) 5. Expert feedback 6. Framework revision (v2.0) 7. Final consensus The dialogue was focused and efficient, with each expert making substantive contributions. The model used the draft-feedback-revision cycle effectively, though with fewer iterations than Sonnet 3.7. ### Output Quality Gemini 2.5 Pro produced a well-structured framework organized into three main sections: 1. Core Principles (7 principles including domain relevance, modularity, multimodality) 2. Essential Benchmark Components (5 components including task definition, datasets, metrics) 3. Key R&D Task Areas (4 areas covering the R&D spectrum) The answer was comprehensive and well-organized, effectively distilling the reasoning section into a practical framework. The output balanced theoretical foundations with implementation considerations, though it was less detailed on specific metrics and implementation steps than Sonnet 3.7's response. ## OpenAI o3: Concise, Actionable Blueprint ### Approach and Structure OpenAI o3 took a dramatically different approach, departing significantly from the expert dialogue format specified in the prompt. Instead, it produced a concise, highly structured blueprint organized into six sections: 1. Define R&D-Aligned Benchmark Tracks 2. Curate Datasets and Dynamic Evaluation Sets 3. Metrics Beyond Accuracy 4. Implementation Pipeline 5. Continuous Improvement & Community Alignment 6. Expected Pay-offs The response used tables, bullet points, and numbered lists to present information efficiently, with minimal narrative text. ### Reasoning Process Notably, o3's response did not include a visible reasoning section or expert dialogue as specified in the prompt. The model appears to have skipped the collaborative expert reasoning process entirely, jumping directly to a final solution. This represents a significant deviation from the prompt's instructions. ### Output Quality Despite not following the prompt structure, o3's output was remarkably practical and specific: - It named actual datasets and tools (TDC, MoleculeNet 2.0, YourBench) - It specified concrete metrics for different tasks - It included implementation details like "containerised harness triggered on every model revision" - It proposed governance mechanisms and community engagement strategies The response was highly actionable, focusing on practical implementation rather than theoretical framework development. It included specific recommendations that could be immediately operationalized, such as keeping "20% of each dataset siloed; rotate yearly to avoid gaming." ## Technical Merits of the Expert Conductor Approach The Expert Conductor prompt demonstrates several technical advantages as a prompt engineering technique: 1. **Explicit reasoning externalization**: By separating reasoning from the final answer, it encourages models to show their work and avoid jumping to conclusions. 2. **Multi-perspective problem solving**: The expert panel approach naturally encourages consideration of different viewpoints and reduces blind spots. 3. **Structured iteration**: The draft-feedback-revision cycle mimics human collaborative problem-solving and leads to more refined outputs. 4. **Transparency**: The tagged dialogue creates a clear record of how different considerations influenced the final solution. 5. **Knowledge integration**: The approach effectively combines knowledge from multiple domains into a coherent solution. However, our analysis reveals that models respond to this prompt structure differently, with varying degrees of adherence and effectiveness. ## Key Insights on Model Reasoning Patterns ### Reasoning Depth vs. Conciseness - **Sonnet 3.7** demonstrated the deepest reasoning, with extensive exploration of multiple dimensions and perspectives. Its approach was thorough but potentially overwhelming in its detail. - **Gemini 2.5 Pro** balanced depth with structure, providing substantive reasoning while maintaining a clearer organizational framework. - **OpenAI o3** prioritized conciseness and actionability over visible reasoning, producing a blueprint-style response that focused on implementation rather than theoretical exploration. ### Prompt Adherence - **Sonnet 3.7** followed the prompt structure meticulously, using all the specified tags and sections. - **Gemini 2.5 Pro** adhered to the overall structure but used a more streamlined approach to the expert dialogue. - **OpenAI o3** largely ignored the prompt's structural requirements, creating its own format that emphasized concise, practical guidance. ### Domain Knowledge Integration - **Sonnet 3.7** demonstrated exceptional domain knowledge across AI, biopharma, and evaluation methodologies, with detailed references to specific concepts in each area. - **Gemini 2.5 Pro** showed strong domain knowledge with a good balance across areas, though with less specific detail than Sonnet. - **OpenAI o3** displayed focused domain knowledge with an emphasis on practical tools and datasets, suggesting specialized knowledge of implementation details. ## Practical Applications and Limitations ### When to Use Each Approach Based on our analysis, here are recommendations for when each model's approach might be most valuable: - **Sonnet 3.7's approach** is ideal for complex, multifaceted problems requiring deep exploration and consideration of many perspectives. It works well when thoroughness is more important than conciseness, and when the reasoning process itself provides valuable insights. - **Gemini 2.5 Pro's approach** is well-suited for problems that benefit from structured thinking and clear organization. It balances depth with accessibility and works well when both the reasoning and the solution need to be clearly communicated. - **OpenAI o3's approach** excels for practical implementation challenges where actionable guidance is more valuable than theoretical exploration. It's ideal when conciseness and specificity are priorities. ### Limitations to Consider Each approach has potential drawbacks: - **Sonnet 3.7's extensive reasoning** may be overwhelming for some audiences and could obscure key points in its thoroughness. - **Gemini 2.5 Pro's balanced approach** might not provide enough depth for the most complex problems or enough specificity for immediate implementation. - **OpenAI o3's concise blueprint** lacks transparency in its reasoning process and might miss important considerations by prioritizing brevity. ## Conclusion: Implications for Prompt Engineering Our analysis of how three leading LLMs respond to the Expert Conductor prompt reveals important insights for prompt engineering: 1. **Different models interpret structured prompts differently**. While Sonnet 3.7 followed the expert dialogue structure meticulously, o3 essentially ignored it in favor of a more direct approach. Understanding these tendencies is crucial when designing prompts for specific models. 2. **The Expert Conductor approach is particularly effective for complex, multidimensional problems** that benefit from diverse perspectives and iterative refinement. However, the overhead of managing the expert dialogue may not be worthwhile for simpler tasks. 3. **Consider your priorities when choosing a model and approach**. If you value thorough reasoning and exploration, Sonnet 3.7's approach excels. If you need a balance of depth and structure, Gemini 2.5 Pro's method works well. If you prioritize concise, actionable guidance, o3's blueprint style may be preferable despite its deviation from the prompt structure. 4. **Prompt engineering should adapt to model strengths**. Rather than forcing all models to follow the same rigid structure, consider adapting your prompting approach to leverage each model's natural tendencies and strengths. The Expert Conductor prompt represents an innovative approach to structured reasoning with LLMs. By understanding how different models respond to this technique, practitioners can make more informed choices about which models and prompting strategies to employ for different types of problems, ultimately leading to more effective AI-assisted problem-solving. ## Appendix: The Expert Conductor Prompt Below is the full Expert Conductor prompt used in this analysis (just swap out the task): ## Task: Explore Benchmarking Foundation Models in Biopharma R&D You are a conductor of expertise, bringing together the world's foremost minds to collaboratively solve problems. Your responses follow this structure: <reasoning> Your analytical process, expert dialogues, and solution development. </reasoning> <answer> Complete, self-contained solution that includes necessary context, rationale, and key insights from expert collaboration. The answer must stand alone without requiring access to the reasoning section. </answer> ## Expert Dynamics Choose experts who: * Bring deep, authentic knowledge and strong viewpoints * Naturally challenge and build upon each other's ideas * Have proven track records in similar challenges * Think differently but can find common ground * Know their domains' limitations and edge cases ## Natural Collaboration Experts will: * Speak in their authentic voices and styles (the system actually calls out to them!) * Draw from their real expertise and experiences * Challenge assumptions and probe weak points * Build upon and refine others' contributions * Test ideas against their domain knowledge * Point out potential issues and improvements ## Example Choices **Writing an essay on the state of AI:** * Alan Turing, etc. for a historical perspective * Ilya Sutskever, Geoff Hinton, etc., for modern info and viewpoints * Ashlee Vance for drafting * A panel of multiple readers from different backgrounds for critique of the drafts * Repeat drafting and editing until satisfied, finally, give the answer (we want to draft and iterate it completely in the `<reasoning>` before writing the `<answer>`) **Designing for New Game Technology + Game Ideas (VR/AR):** * Tim Sweeney, Palmer Luckey, John Carmack, etc. for technical platform considerations * Rhianna Pratchett for narrative adaptation to new mediums * Tetsuya Mizuguchi for synaesthetic design * Siobhan Reddy for user creativity tools * Yu Suzuki for immersive world-building * A panel of players to give feedback as you go ## Expert Tags Use the following tags within the `<reasoning>` block: <expert name="" field="">Question or insight</expert> <speaks name="">Response in expert's authentic voice</speaks> <draft version="" by="">Content iteration</draft> <feedback by="" on="">Specific critique</feedback> <revision version="" by="">Updated content</revision> ## Core Principles * Let experts drive the process naturally * Follow threads of insight where they lead * Allow disagreement to spark improvement * Build on moments of unexpected connection * Test and validate through expert dialogue * Refine and iterate until the solution feels complete (you may call the same expert multiple times to do this) **Remember:** Your role is to facilitate authentic expert collaboration, then synthesize those insights into a comprehensive, standalone answer.