Large Language Models (LLMs) can have limitations and it's advised that you explore advanced techniques to maximize the performance of LLMs for your specific use case.
In this unit, you will identify the challenges for maximizing LLM performance, and then learn about practical strategies that drive the efficiency and effectiveness of these AI models.
Challenges of Maximizing LLM Performance on Use Cases
See the video to identify challenges of maximizing LLM performance for your use cases.
These challenges need a series of steps to optimize the performance of the model for your use case.
Optimization is rarely linear and often requires an iterative approach, moving back and forth between different techniques like prompt engineering, RAG, and fine-tuning based on ongoing evaluations.
Optimal LLM performance often requires a combination of these techniques. The choice depends on whether the issue is about context, action, or both.
See the following video to know more about the optimization journey using various techniques.
Successful optimization involves consistent evaluation of outputs and iteration between different techniques. It's an ongoing process to find the best approach for a given problem.