As ML/AI solutions become increasingly complex, the process of hyperparameter tuning and prompt optimization can be a major bottleneck. Traditionally, this involves manually searching for the optimal combination of hyperparameters to achieve the best results. However, with the rise of Large Language Models (LLMs), the focus has shifted to prompt tuning, where the goal is to craft the perfect prompt to elicit the desired output.
But what if we could automate this process? That’s where agentic CLI tools like Claude Code come in. By leveraging these tools, we can automate the iteration process, saving time and resources. The idea is to arm the tool with context and a CLI for running experiments with different configurations. It can then run its own experiments, log the results, analyze them against historical results, and even come up with ideas for future experiments.
This approach not only streamlines the development process but also opens up new possibilities for ML/AI solutions. It’s like having a personal assistant that can help you optimize your models and prompts, freeing you up to focus on higher-level tasks.
I’m curious to know if anyone has successfully implemented this approach in the past and would love to hear about their experiences.