A Framework for Choosing the Right LLM
At the core of every AI application lies a large language model (LLM). LLMs are the highly skilled translators of the AI world. Just as a human brain understands and generates language, LLMs do the same for AI—analyzing vast amounts of data, learning the nuances of human language, and applying its knowledge to generate human-like text. Whether it's a chatbox responding to a customer query, or an AI generating a customer email, the LLM is hard at work behind the scenes.
As you launch into your AI explorations and start developing AI applications, it's important to know the differences between the many LLMs available to you.
This article provides a framework to help individuals and businesses select the right LLM to suit their AI project's objectives. We cover six key decision-making factors and run through the advantages and disadvantages of some of the top models available today.
Not all LLMs are created equal
There are considerable differences between the LLMs available to you, and selecting the right one is a crucial step in turning your AI idea into a reality. Different tasks require different capabilities, and your choice can significantly influence the capabilities and performance of your AI application, impacting your business outcomes.
Some models may excel in generating creative content, while others might be better suited for analyzing and processing complex data. Having the freedom to select the right LLM based on your AI's requirements allows for a more tailored and effective application.
Tailoring your AI to your unique requirements
The first step in choosing an LLM is to look at your specific use case. The tasks your AI app needs to execute, the scale of your operation, and the data sources you plan to upload to your app are all critical factors to consider. These elements will have a significant influence on which LLM best fits your app.
Evaluating your LLM choices—6 key considerations
As you evaluate LLM options, you'll need to consider the following factors:
- Size and capabilities—LLMs can vary significantly in scale and abilities. Larger models often provide superior capabilities and bigger prompt token size.
- Efficiency and speed—Consider vital metrics like accuracy and response speed, particularly if your application requires swift responses.
- Security—The security protocols and privacy measures of the LLM are critical, especially if your app will handle sensitive data. It's crucial to ensure that the LLM you choose respects data privacy, offers secure data storage, and aligns with your organization's security standards and legal requirements.
- Training data and knowledge limitations—The training data used by an LLM profoundly impacts its knowledge and capabilities. It's important to understand the limitations of LLMs and ensure the one you choose aligns with your objectives.
- Adaptability—Some LLMs allow for custom training, enabling you to tailor the model to your specific needs and tasks.
- Availability and cost—LLMs differ in terms of availability and cost. If you're choosing a custom LLM, some are freely accessible, while others incur a fee. Your selection should align with your budgetary constraints.
Exploring the Leading LLMs
While GPT-4, Cohere, and similar LLM models offer impressive capabilities, it's worthwhile exploring other contenders. Each model has its unique strengths and capabilities that might align better with your specific needs.
Let's take a closer look at some of the large language models available, their unique capabilities, and best use cases.
- GPT-4 (32K Context)—Known for its advanced capabilities in generating human-like text, GPT-4 is ideal for tasks requiring long, coherent texts. It can handle a variety of tasks from translation to question answering. Consider using GPT-4 if your application involves content generation at a professional level.
- GPT-3.5 (16K Context)—While not as powerful as GPT-4, GPT-3.5 still offers impressive capabilities. It's suitable for applications where moderate accuracy is acceptable, and the 16K token limit allows for longer conversations compared to previous models.
- GPT-4 Turbo w/Assistant API (Beta)—This model combines the best of GPT-4 and ChatGPT, offering a more cost-effective solution while maintaining high-quality outputs. It's an excellent choice when considering cost and performance.
- Anthropic Claude 2.1—This is a versatile model that excels in several areas, including summarization, answering questions, and emotion detection. It's particularly efficient and responsive, making it a great choice for real-time applications.
- Anthropic Claude 3 Opus—This model builds on Claude 2.1, offering improved performance and accuracy. It is particularly useful for tasks that require a deep understanding of language and context.
- Anthropic Claude 3 Sonnet—Similar to Opus, Sonnet also provides enhanced performance and accuracy. Its strength lies in tasks involving creative text generation, making it suitable for tasks like content creation and editing.
- LLAMA2 13B Chat (4K Context)—This model is trained on a diverse range of Internet text. However, unlike most models, LLAMA2 can write about topics it was not specifically trained on by using a mixture of licensed data, data created by human trainers, and publicly available data.
- LLAMA2 70B Chat (4K Context)—Similar to LLAMA2 13B, but with a higher token limit, this model can generate much longer pieces of text. It's an excellent choice for tasks that require detailed, in-depth responses.
- Llava 13b—This model balances cost and performance, ideal for general-purpose tasks. It's a good option when you need a versatile model that won't break the bank.
- Cohere Command—Known for its speed and efficiency, Cohere Command is ideal for real-time applications. It shines in tasks that require fast, short responses, such as chatbots for customer support systems.
- Cohere Command Light—A lighter version of Cohere Command, this model is designed for applications where speed is more important than accuracy. It's a good choice for real-time systems where you need to balance performance and cost.
- Cohere Command-R—This model is designed for research purposes and offers the highest quality of responses in the Cohere Command series. It's the best choice if you're conducting research or any tasks that require the highest level of accuracy.
Making the right LLM choice
If you feel intimidated by the large number of LLM choices, remember that you can always change the LLM to suit your purpose and as your needs evolve and as new LLMs are released. The best approach is to start experimenting!
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