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How to Choose an LLM

Different LLMs have varying strengths and weaknesses. Understanding the strengths and weaknesses of different language models is crucial, as it allows you to select the most efficient tool for your specific needs. When you evaluate and test various LLMs, you are essentially comparing their ability to answer different types of questions, their performance, and their cost-effectiveness. This comparison isn't merely an academic exercise but a critical step in identifying which model can best serve your needs in real-world applications. This process is straightforward and achievable with the right tools and guidance, so everyone can benefit from the advancements in artificial intelligence. How do I figure out which LLM is best for my business? Choosing an LLM need not be a daunting task. With the right planning and tools, you can find one or more LLMs to suit your needs in a matter of days; not months. We'll discuss some key factors you should consider when evaluating different LLMs. Cost The cost of using an LLM is often a crucial deciding factor. Some models may be cheaper to use, while others may come at a higher price but offer better performance. Understanding your budget and the expected return on investment will help you make an informed decision. Accuracy Accurate responses are essential when it comes to language models. Some LLMs may excel at answering specific types of questions but struggle with others. Evaluating the accuracy of different models is crucial, as it will determine how well they can perform in real-world scenarios. Performance The performance of an LLM refers to its ability to generate accurate and relevant responses within a reasonable timeframe. Some models may take longer to process and generate responses, impacting their overall performance. It's essential to consider the speed of different LLMs to ensure they can meet your needs. Data Security When dealing with sensitive information, data security is a top priority. Some language models may require access to external servers or use cloud-based services, which may pose a potential security risk. It's important to understand the security protocols of different models and choose one that aligns with your data privacy policies. How to Evaluate and Test Different LLMs Now that we have identified some key factors to consider, let's look at how you can evaluate and test different LLMs in a matter of minutes. 1. Start by identifying your specific use case for an LLM. This will help you narrow down the list of available options. A great first use case is an employee assistant since it is straightforward and more than likely you have the data to support the test. We chose this use case in Comparing Large Language Models for Your Enterprise: A Comprehensive Guide. 2. Gather the documents related to your use case. These documents will serve as the basis for generating questions to test the LLMs. If you want to run a similar test to ours you can use your employee handbook or another resource that you are familiar with. 3. Import your documents into Krista and write down the questions that you would like to ask of your data. If you have FAQs available based on your document, then you can use those questions, different ones, or a combination. 4. Choose 2-3 LLMs that you want to compare and run each question through them, recording their responses. Krista provides you with a conversational interface to ask questions about your document sets. 5. Evaluate the accuracy, performance, and cost of each model's responses. Consider any other relevant factors, such as data security, when making your final decision. 6. Repeat the process with different sets of questions and documents to thoroughly test each LLM. If you want to connect a system like a ticketing system, CRM, or email inbox, contact us for help. The Importance of Continuously Testing LLMs It's important to remember that language models are continually evolving and improving. What may be the best option today may not necessarily be the case in a few months or years. That's why it's crucial to continuously test and evaluate different LLMs, even after you have made your initial selection. This process will ensure that you are always using the most effective and efficient model for your specific needs. Then, if you do find an LLM that performs better or at lower costs you need to be able to quickly and easily convert from one to another. Hard coding an LLM into a process or an application will lock you into a single vendor and increase your overall technical debt. Using a platform like Krista can help you easily switch between models and avoid getting locked into a single vendor. Link to paper - https://krista.ai/comparing-large-lan... Link to raw results -https://docs.google.com/spreadsheets/...