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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,
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 repository is a comprehensive set of tutorials and examples for building software powered by large language models. It serves as an application development guide and a prompt engineering framework, providing instructional content for integrating model logic with user interfaces and external data sources. The project provides technical walkthroughs for specialized workflows, including the implementation of retrieval augmented generation using vector databases and semantic search. It includes guidance on adapting pre-trained model weights through fine-tuning with private datasets and the o
auto-dev is an AI-native software engineering tool and multi-agent development platform designed to automate the entire software development lifecycle. It functions as an autonomous orchestrator that manages AI-driven coding, testing, and infrastructure configuration through declarative agent chains. The project is built on a Kotlin Multiplatform AI framework, allowing agent logic to run across diverse environments and device interfaces. The platform implements the Model Context Protocol to exchange tools and project information with external AI services. It distinguishes itself through the u
This repo provides the source code & data of our paper: Unleashing the Potential of Large Language Models as Prompt Optimizers: An Analogical Analysis with Gradient-based Model Optimizers.
The main features of rucaibox/gpo are: Prompt Optimization.
Projects with overlapping indexed features include: nirdiamant/prompt_engineering — This project is a comprehensive guide and framework for designing, optimizing, and securing inputs to improve the… mshumer/gpt-prompt-engineer — This project is an automated prompt engineering and optimization tool designed to iteratively create, test, and refine… 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… microsoft/lmops — LMOps is a research-driven operations framework for optimizing the deployment, fine-tuning, and performance of large… hwxu20/gps — This is the implementation of the paper GPS: Genetic Prompt Search for Efficient Few-shot Learning. GPS is an…