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TextGrad is a differentiable text optimization library and framework designed for simulated language model backpropagation. It functions as a textual gradient engine that treats language model feedback as gradients to iteratively refine prompts and unstructured text variables. The system utilizes a computation graph to trace errors from a defined loss function back to input text, allowing it to determine specific improvements. It differentiates itself by implementing natural-language backpropagation and gradient aggregation, which merges multiple pieces of textual critique into consolidated i
DSPy is a declarative programming framework designed for building complex language model applications. It treats model interactions as modular, composable programs, allowing developers to define task logic through typed class schemas rather than relying on manually written prompts. By organizing workflows into hierarchical, reusable Python objects, the framework enables the construction of sophisticated AI systems that manage state and execution flow independently. The framework distinguishes itself through an automated optimization engine that iteratively refines prompt instructions and few-
This project is an automated prompt engineering and optimization tool designed to iteratively create, test, and refine prompts using a language model to improve output quality. It functions as a framework for generating candidate prompts and ranking their performance through correctness matching and ELO-based ratings. The system includes capabilities for model distillation, generating high-quality example pairs from frontier models to create training data for smaller models. It also provides tools to condense prompts for smaller models and transform instruction-tuned prompts into completion-b
This project is a comprehensive guide and framework for designing, optimizing, and securing inputs to improve the accuracy and reasoning of large language model outputs. It provides core methodologies for implementing logical reasoning steps, example-based learning, and reusable template systems. The framework distinguishes itself through a focus on security guardrails and ethical auditing, implementing primitives to prevent adversarial prompt injection attacks and identify biases. It also emphasizes structured generation, using persona assignment and negative constraints to control the tone,
official code for "Large Language Models as Optimizers"
The main features of google-deepmind/opro are: Prompt Optimization, Prompt Optimization Tools.
Projects with overlapping indexed features include: stanfordnlp/dspy — DSPy is a declarative programming framework designed for building complex language model applications. It treats model… zou-group/textgrad — TextGrad is a differentiable text optimization library and framework designed for simulated language model… mshumer/gpt-prompt-engineer — This project is an automated prompt engineering and optimization tool designed to iteratively create, test, and refine… nirdiamant/prompt_engineering — This project is a comprehensive guide and framework for designing, optimizing, and securing inputs to improve the… datawhalechina/llm-cookbook — This repository is a comprehensive set of tutorials and examples for building software powered by large language… phodal/auto-dev — auto-dev is an AI-native software engineering tool and multi-agent development platform designed to automate the…