Choosing the Right LLM: How to Test and Select AI Models for Quality, Speed, and Cost Efficiency
TL;DR: This article delves into the analysis of three leading LLMs—Claude 3 Haiku, Claude Command R, and OpenAI ChatGPT 4o—demonstrating the importance of such comparisons. And discusses how MindStudio, which include built-in debuggers and LLM profilers, make it easy to optimize your AI app.
Intro
For businesses exploring the potential of generative AI, one thing is becoming increasingly clear: choosing the right AI model is paramount for success. Whether you're a seasoned tech company or just beginning to integrate AI into your operations, the sheer variety of Large Language Models (LLMs) can feel overwhelming. This article is designed to be your guide, offering a practical framework for testing and selecting the best LLM for your specific needs, focusing on quality, speed, and cost-efficiency.
For those of you that know me, in January 2024, I decided to dig deep into generative AI. Not just read and watch information, but follow my mental model: “Consume, Digest, Reflect, and Share.” By sharing, I hope to shorten your journey. Let me share a story.
I decided to become an AI developer and have since become a Certified MindStudio Developer—just one of a handful before they rebranded the certification to Certified MindStudio Expert Level 3. I’m passionate about helping founders and businesses use and bring future-forwarding technology to deliver value for you, your children, and your children’s children.
As someone who also teaches university engineers, scientists, and doctors how to bring products to market, I decided to build—and am still in the process of building—an AI app that speeds them along to their ultimate success. Specifically, I’m designing an AI to go from Idea to Business - Phase I:
• The AI is designed to assist deep tech companies originating from universities or research institutions. • The AI’s role is to help companies develop the upper right portion of the business model canvas, focusing on the value proposition, customer segments, channels, and relations. • The AI will help develop an initial value proposition and suggest potential customer segments with specific titles of customers for validation through interviews.
In building this model, I was stunned by the wide variety in costs and quality across the various AI systems. One model provided a response twice as large as the other; and the bigger response was much more comprehensive. (426 characters, 193 and 167)
I’d never really thought about it before, but for obvious reasons, LLM companies do not provide an easy way to compare their models to competitors. This means that for your specific needs, you will want to optimize the quality, speed, and cost to generate a response.
Fortunately as a MindStudio Expert, I have access to a powerful AI workflow app development system. MindStudio includes tools that professionals need, like a built in debugger, a profiler to compare models for cost, speed and quality; logic flow; drag and drop design and much more.
Scenario Tested:
Imagine a startup, RoboPet, that is poised to revolutionize companionship and therapy through AI-powered robotic pets. The goal is to develop a value proposition and validate customer segments. The task is to generate two value propositions for the business idea, followed by a list of specific business hypotheses to be tested during customer interviews, so that you can decide if your idea is a good one or not. For the exact prompts and results see: https://docs.google.com/document/d/1S9GNWSFzePegny8SZb-F8KsnaaYPk9JXrlaf25pp72c/edit
Response Analysis
The following are the responses from each of the three LLMs reviewed; this approach can be expanded to the 8 companies and 30 models that MindStudio currently supports (and more scheduled on the product road map.)
Claude 3 Haiku
Value Propositions:
RoboPet offers personalized emotional support and therapeutic benefits through AI-powered robotic pets.
RoboPet’s AI-driven pets combine technology and emotional intelligence to promote mental health and well-being.
Business Hypotheses to Validate:
Elderly individuals living alone.
Individuals with mental health conditions.
Parents of children with autism or developmental disorders.
Healthcare professionals.
Individuals with physical disabilities or chronic illnesses.
Pros:
• Clear and concise value propositions.
• Specific and diverse customer segments.
• Emphasizes both emotional support and therapeutic benefits.
Cons:
• Lack of detailed hypotheses to test during customer interviews.
• Some redundancy in value propositions.
Claude Command R
Value Propositions:
AI-driven robotic companions offering personalized emotional support and therapy.
High-tech companionship integrating healthcare to enhance mental well-being.
Business Hypotheses to Test:
Demand for AI-powered robotic companions among specific demographics.
Desirability of personalized emotional support and learning capabilities.
Interest from healthcare professionals.
Improvement in users’ mental health and reduced loneliness.
Willingness to adopt AI technology for therapy and support.
Pros:
• Focus on personalized support and healthcare integration.
• Clear hypotheses aimed at understanding market demand and user experience.
• Emphasis on the practical application of technology.
Cons:
• Hypotheses are somewhat broad and may need further refinement.
• Limited specificity in customer segment titles.
OpenAI ChatGPT 4o
Value Propositions:
AI-powered robotic companions offering personalized emotional support and integrating with healthcare systems.
Advanced AI-driven pets providing reliable companionship and therapeutic aids.
Business Hypotheses to Test During Customer Interviews:
Emotional Support Effectiveness: Users’ improvement in emotional well-being.
User Interaction and Adaptability: Value of RoboPet’s adaptability.
Healthcare Integration: Value to healthcare providers.
Accessibility and Reliability: Perception as an accessible and reliable therapeutic aid.
Market Demand and Willingness to Pay: Market demand and customer willingness to pay a premium.
Pros:
• Detailed and actionable hypotheses with specific interview questions.
• Comprehensive approach covering various aspects (emotional support, adaptability, healthcare integration, accessibility, market demand).
• Emphasis on user feedback and validation.
Cons:
• May be perceived as overly detailed for initial high-level validation.
• Hypotheses are focused on specific areas, which might miss broader market insights.
The Importance of Multi-LLM Testing
The analysis highlights the importance of testing prompts across multiple LLMs. Each model has its strengths and weaknesses, and understanding these can significantly impact your project’s success. And if you are building a many app with many AI steps than you need a way to optimize each step. For example you don’t want to use an expensive and slow model to summarize text, when a quick and inexpensive model delivers good results.
Quality: The depth and clarity of responses vary. OpenAI ChatGPT 4o provided the most detailed and comprehensive analysis, making it suitable for thorough research and detailed planning. Claude 3 Haiku offered concise and clear outputs, which are beneficial for quick insights and high-level overviews.
Speed: Depending on the complexity of the task, some models might generate responses faster. For instance, simpler and more direct prompts might be handled efficiently by Claude 3 Haiku, whereas more complex scenarios might benefit from the depth provided by OpenAI ChatGPT 4o, albeit possibly at a slower speed.
Cost: The computational resources required by each model can affect the cost. More detailed models like OpenAI ChatGPT 4o might incur higher costs due to their depth and comprehensiveness. In contrast, models like Claude 3 Haiku might be more cost-effective for simpler, less detailed tasks.
Enhancing the Process with MindStudio
MindStudio is an AI workflow platform that includes built-in debuggers and LLM profilers for analyzing speed, cost, and response quality. By integrating MindStudio into your workflow, you get:
Debugging: Quickly identify and resolve issues within your prompts to ensure optimal performance across different LLMs.
Profiling: Analyze the performance of each LLM in terms of speed, cost, and response quality, helping you make data-driven decisions about which model to use for specific tasks.
Optimization: Continuously improve the quality of your outputs by iterating on prompts and leveraging the detailed insights provided by MindStudio’s profiling tools.
Matching Needs to Models
To match your specific needs, consider the following:
For Quick, High-Level Insights: Claude 3 Haiku provides concise and specific outputs, making it ideal for quick decision-making and high-level brainstorming sessions.
For Balanced Detail and Practicality: Claude Command R strikes a balance between detail and practicality, offering a good mix of actionable insights and specific guidance.
For Comprehensive, In-Depth Analysis: OpenAI ChatGPT 4o is suitable for in-depth research and detailed hypothesis testing, especially when the task requires a nuanced and thorough approach.
Conclusion
Choosing the right LLM is crucial for any AI project. As we've seen, different models excel in different areas like speed, cost-effectiveness, and the depth of their responses. There's no one-size-fits-all solution, and what works best for a quick, high-level task might not be ideal for a complex, nuanced project.
That's why it's essential to adopt a data-driven approach. Don't rely on marketing hype or assumptions. Test your specific prompts across multiple LLMs, carefully analyze the results, and consider the following:
Define Your Priorities: What matters most for your project: speed, cost, quality, or a balance of all three?
Leverage the Right Tools: Platforms like MindStudio, with built-in debuggers and profilers, can streamline the testing and optimization process, helping you make informed decisions.
Iterate and Improve: The LLM landscape is constantly evolving. Stay updated on new models and features, and continuously experiment to find what works best for your evolving needs.
By embracing a strategic and adaptable approach to LLM selection, you can unlock the full potential of AI and drive impactful results for your projects and business