Playbook: How to choose the best LLM for your own usecase?

0. Introduction

Selecting the appropriate Large Language Model (LLM) is pivotal for tasks requiring nuanced understanding and generation of text. This guide delineates essential criteria—accuracy, cost, reliability, and privacy—to assist in navigating through the complex ecosystem of LLMs. Each factor plays a critical role in determining the efficacy and applicability of an LLM to your specific needs, ensuring a well-informed decision that aligns with both technical requirements and strategic goals.

1. Accuracy

1.1 Definition

Accuracy in the context of Large Language Models (LLMs) refers to the model's performance on a specific task. This performance is often measured against pre-determined benchmarks to assess the LLM's capabilities in various domains.

1.2 Benchmarks

When assessing the accuracy of Large Language Models (LLMs), it's crucial to consider their performance on specific tasks. Benchmarks play a vital role in this evaluation, providing standardized challenges that test different aspects of an LLM's capabilities. For instance, as reported by Anthropic, various models like Claude 3 and GPT-4 show distinct performance levels across tasks such as mathematics, language understanding, and common knowledge questions. 

Platforms like the LMSYS Chatbot Arena, a crowdsourced open platform for LLM evaluations, enrich this landscape by ranking LLMs based on over 400,000 human preference votes using the Elo system. They offer insights into user preferences and model effectiveness in conversational contexts. Learn more about the LMSYS Chatbot Arena here.

A challenge with benchmarks is data contamination, where a model might have been trained on data that includes the test problems, leading to artificially high performance.

1.3 Arena

 'Arenas' inspired by competitive gaming, such as chess, have been developed where models like ChatGPT, Claude, and others are pitted against each other to rank their effectiveness. 

The LMsys Chatbot Arena, for example, allows various models to compete, producing an ELO-like score that indicates their relative strengths. This method, while insightful, does not always clarify why one model outperforms another, emphasizing the need for a more nuanced approach to evaluating model performance.

For a detailed look into benchmarks and rankings, Anthropic's Claude 3 family and the LMsys Chatbot Arena leaderboard provide detailed insights into the current standings of various models.

Schaeffer's paper highlights benchmarks and their potential drawbacks, such as the risk of data contamination. It proposes that pre-training on the test set is all one might need to achieve impressive results.

3. Cost

The cost of Large Language Models (LLMs) varies significantly between open-source and commercial offerings. Open-source options like Mistral AI's models can be more affordable per-token basis, with pricing starting at $0.25 per million tokens. 

3.1 LLM Pricing Structures

3.1.1 Open-Source Models

A cost analysis must weigh the initial setup investments against the operational costs. Open-source LLMs are generally less expensive per token basis but require more upfront customization and infrastructure development, which could lead to higher initial technical costs. 

3.1.2 Commercial LLM

Commercial models, while potentially higher in per-token cost, offer simplified integration. For example, ChatGPT has plans starting at $20 per month, offering additional tools and support. Google's Gemini, on the other hand, provides a pay-as-you-go tier starting at $0.000125 per 1K characters. 

These models cater to different operational scales and integration needs, underscoring the importance of evaluating setup and long-term costs relative to specific project requirements and privacy considerations.

4. Reliability

4.1 Rate Limits

Reliability in the context of LLMs is often dictated by rate limits, which control the volume of interactions per time unit to prevent abuse and ensure fair access. Different tiers offer varying limits, affecting how many tokens per minute can be processed. OpenAI's rate limit system, detailed in its Rate Limits Guide, outlines these restrictions and provides strategies to manage them efficiently.

4.2 Service Level Agreements (SLAs)

Unlike traditional software services, many LLM providers, including OpenAI, currently do not offer public SLAs to guarantee uptime or performance, which can be a concern for applications requiring high reliability.

OpenAI, however, is working towards publishing SLAs and offers a Status Page for real-time operational updates. This page helps users track the system's health and performance, which is critical for those with stringent latency needs.

In high-volume scenarios, companies need to consider these factors as generous rate limits may still fall short for extensive operations, and the absence of formal SLAs means there's no guaranteed model availability or consistent response times.

5. Privacy Concerns with LLMs

Privacy is a significant concern when interacting with LLMs, as data fed into these models may be utilized to further their training. This process has the potential to expose sensitive information to unintended parties inadvertently. 

In scenarios where users disclose private or proprietary information, there is a risk that competitors or external entities could access such data. 

The privacy implications are considerable, necessitating careful consideration of the type of data shared with LLMs and the choice of models for tasks involving confidential information.

6. Playbook: On How to evaluate

6.1 Accuracy: Custom Benchmark Development

Creating domain-specific benchmark data is critical for accurately evaluating an LLM's performance. By annotating a tailored dataset with expected responses, you establish a clear metric for success that is directly aligned with your unique requirements. This approach ensures the LLM's effectiveness is measured against the nuances of your specific domain.

6.2 Cost

6.2.1 Estimating Excepted Volume

Begin by estimating the expected volume of usage for the LLM. This foundational step informs variable and fixed cost calculations, ensuring budgetary alignment with project needs.

6.2.2 Variable Cost Analysis

Examine the variable costs associated with each provider, typically tied to the number of tokens processed or API calls made. This granular analysis allows for accurate operational budgeting.

6.2.3 Fixed Cost Evaluation

Identify fixed costs, such as monthly or annual subscriptions and initial setup fees, to fully understand the financial commitment required for LLM integration.

6.3 Reliability

6.3.1 Multi-Provider Strategy

It's advisable not to rely solely on a single LLM provider for enhanced reliability. Using multiple providers ensures continuity in case one service becomes unavailable.

6.3.2 Orchestration Frameworks Support

Frameworks like LangChain and LLamaIndex offer orchestration capabilities that seamlessly facilitate using multiple LLMs, allowing for an automatic fallback mechanism.

This approach to reliability maximizes uptime and service consistency across LLM applications.

6.4 Privacy

For projects where privacy is important, leveraging open-source models is recommended. These models allow for in-house operation, minimizing the risk of confidential data exposure to external entities. Open-source solutions provide the flexibility to adapt security measures to your specific requirements, ensuring data privacy and protection.

7. Conclusion

When selecting a Large Language Model (LLM) for your project, it's essential to consider factors such as accuracy, cost, reliability, and privacy. Understanding and evaluating these core aspects can ensure a strategic fit for their needs. Given the fast-paced advancements in LLM technology, a strategic, informed approach is crucial.

Custom AI leverages extensive experience in evaluating these criteria to guide you toward the most suitable LLM option for your specific requirements. Our approach ensures that your choice is technically sound and strategically aligned with your goals, providing a solid foundation for your AI-driven initiatives.

8. References

1. Hugging Face. (n.d.). Open LLM Leaderboard. Hugging Face Spaces. Retrieved from huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard.
2. Anthropic. (2024, March 4). Introducing the next generation of Claude. Retrieved from anthropic.com/news/claude-3-family.
3. Schaeffer, R. (2023, September 19). Pretraining on the Test Set Is All You Need. Retrieved from arxiv.org/pdf/2309.08632.pdf.
4. LMsys. (2024, March 13). Chatbot Arena Leaderboard. Hugging Face Spaces. Retrieved from chatbot-arena-leaderboard.
5. OpenAI. (n.d.). Rate Limits. OpenAI Documentation. Retrieved from platform.openai.
6. OpenAI. (n.d.). Is there an SLA for latency guarantees on the various engines? OpenAI Help Center. Accessed.