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DSPy, which stands for Declarative Self-improving Python, is an open-source framework designed to facilitate the programming of language models (LMs) rather than relying on traditional prompt engineering. It allows developers to rapidly build modular AI systems, offering algorithms for optimizing prompts and weights. Whether constructing simple classifiers, sophisticated retrieval-augmented generation (RAG) pipelines, or agent loops, DSPy provides a compositional Pythonic approach to enhance the quality and reliability of AI outputs.

DSPy

dspy

An open-source Python framework for programming language models, enabling rapid development of modular AI systems with optimization capabilities.

ModelBench AI is a no-code platform designed to facilitate the evaluation and comparison of more than 180 language models. It allows developers, product managers, and prompt engineers to optimize prompts, benchmark models, and trace outputs without requiring coding expertise. Key features include side-by-side model comparison, custom tool integration, prompt engineering with immediate feedback, and comprehensive benchmarking across various scenarios. ModelBench AI aims to accelerate AI development and testing processes, enhancing efficiency and collaboration within teams.

ModelBench AI

modelbench-ai

A no-code platform enabling teams to evaluate and compare over 180 language models, streamlining AI development and testing.

Explore AI Agents Tagged with "prompt optimization"

DSPy

ModelBench AI