Business leaders are starting to see the potential benefits of using generative AI in their organizations, but they may be unsure of how to choose the right Large Language Model (LLM) for their specific needs. With the rapid growth of LLMs and the complexity of the technology, it can be overwhelming to make a decision.
To make the best choice for your business, it’s important to consider the level of sophistication you need in an LLM. There are three levels to consider: a simple application around GPT, an LLM with retrieval-augmented generation (RAG), and running your own models. Each level offers different capabilities and costs, so it’s essential to match the sophistication level with your organization’s goals.
When deciding on the right LLM for your use case, it’s crucial to understand what you want to achieve with the technology. For example, if you’re an ecommerce company looking to improve customer support, a chat interface powered by an LLM may be more cost-effective than human intervention. On the other hand, if you’re a banking application where accuracy is crucial, developing your own model or using a tightly controlled third-party LLM may be the best option.
Regardless of which LLM you choose, monitoring its performance is essential. As technology stacks become more complex, tracking metrics like time-to-token, hallucinations, bias, and drift can help ensure the LLM is performing as expected. Observability tools can provide visibility across the stack and help optimize the technology’s ROI.
The journey of implementing generative AI in your organization can be daunting, but understanding your business needs and selecting the right LLM can lead to short-term benefits and set the stage for future success. By matching the sophistication level of the LLM with your goals and monitoring its performance, you can make the most of this exciting and fast-paced technology.
Overall, choosing the best LLM program for your organization requires careful consideration of your business needs, the level of sophistication required, and the specific use case. By taking the time to evaluate these factors and monitoring the LLM’s performance, you can leverage generative AI to drive success in your organization.
