#Prompt Optimization

AdalFlow provides model-agnostic solutions for building and optimizing large language model (LLM) applications with an efficient and modular design. Its auto-differentiative framework enhances performance in zero-shot and few-shot optimizations, making it suitable for various AI and NLP tasks.

LMOps is a research initiative dedicated to developing AI solutions with foundation models, focusing on enhancing capabilities through Large Language Models (LLMs) and Generative AI. It explores areas such as automatic prompt optimization, extensible prompts, structured prompting, and LLM inference acceleration. The project addresses technologies like context demonstration selection and instruction tuning, improving the functionality and customizability of AI solutions across domains. By providing insights into in-context learning, LMOps significantly contributes to the advancement of AI technologies.

Explore a framework designed to systematically optimize language model prompts and weights, significantly improving the efficiency of multi-stage language models. The system separates program flow from parameters, utilizing LM-driven algorithms to adjust prompts and weights according to specific metrics. It is compatible with models such as GPT-3.5 and GPT-4, offering consistently reliable and high-quality results and minimizing manual prompting. Ideal for those looking for a structured, iterative approach to building complex language model systems.