Show HN: Improve LLM Performance by Maximizing Iterative Development
Palico AI is an LLM Development Framework on GitHub for streamlined LLM app development. It offers modular app creation, cloud deployment, integration, and management through Palico Studio, with various components and tools available.
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The GitHub URL provided contains information about Palico AI, an LLM Development Framework designed to streamline LLM application development for quick experimentation. Users can create modular LLM applications, enhance accuracy through experiments, deploy applications on various cloud providers, integrate with other services, and manage applications using Palico Studio. The framework offers components like Agents for application building, Workflows for intricate control flows, tools for benchmarking and analyzing performance, deployment to docker containers, a Client SDK for connecting to Agents or Workflows, tracing capabilities, and Palico Studio for application monitoring and management. For further details, the Palico AI GitHub page can be accessed.
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9 commentsYou know exactly what goes into the prompt, how it’s parsed, what params are used or when they are changed. You can abstract away as much or as little of it as you like. Your API is going to change only when you make it so. And everything you learn about patterns in the process will be applicable to Python in general - not just one framework that may be replaced two months from now.
Also in general, We are currently in a time of comparatively low iteration. Most companies don't have the tolerance for it anymore and choose cheap one-shot execution at stupid risk, because of FOMO.
Iteration cycles are a function of your inputs; creative potential, vision, energy, runway.
Do not you have a phenomenon akin to overfitting? How do you ensure that enhancing accuracy on foreseen input does not weaken results under unforseen future ones?
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