Projects sharing features with PromptWizard

Learn_Prompting is an educational project focused on prompt engineering, providing the principles and techniques required to craft effective inputs and improve the quality of generative AI outputs. The project covers advanced prompting strategies to enhance reasoning, reliability, and output quality. This includes techniques for task decomposition, chain-of-thought reasoning, and the use of few-shot and zero-shot guidance. It also addresses model security through the study of prompt hacking, vulnerability analysis, and privacy auditing to prevent sensitive data leaks. The scope extends to th

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

This project is a comprehensive framework for building and managing autonomous agent systems. It provides a unified architecture for orchestrating multi-agent societies, where specialized agents collaborate through roleplay to decompose and solve complex tasks. The system integrates language models with external environments, enabling agents to perform real-world actions through a standardized tool-calling abstraction layer. The framework distinguishes itself through its focus on iterative reasoning and data reliability. It employs automated feedback loops to refine agent outputs and self-eva

This project is a comprehensive educational resource and technical guide focused on the development, optimization, and application of large language models. It provides a structured curriculum for mastering prompt engineering, ranging from foundational principles of instruction design to advanced techniques for improving model reasoning, accuracy, and reliability. The guide distinguishes itself by offering deep technical insights into agentic workflows and autonomous system design. It covers the implementation of multi-step reasoning chains, tool integration through function calling, and stat

Self-instruct is a framework for generating synthetic instruction datasets and fine-tuning large language models to improve their instruction-following capabilities. It provides a pipeline for aligning pretrained models with human intentions through a supervised fine-tuning workflow. The system utilizes a synthetic data generator that uses a seed set of tasks to prompt a model to create new instructional data. It includes an instruction dataset curator to remove redundant or low-quality entries, maintaining dataset diversity through a filtered task pool. The framework covers the full alignme

Owl is a framework for agentic workflow automation and multi-agent orchestration. It functions as a system for coordinating autonomous large language model agents to decompose and execute complex tasks through shared communication and collaborative planning. The project distinguishes itself through a multi-modal toolset for processing images, audio, and video, alongside a synthetic data generator that produces domain-specific datasets using self-instruct and verifier loops. It further incorporates a retrieval-augmented generation pipeline framework that integrates long-term memory and real-ti

This project is a framework for the iterative optimization and validation of LLM agent skills. It functions as an agent capability orchestrator and prompt optimizer, utilizing an evaluation framework to measure performance through weighted rubrics and automated rewriting. The system distinguishes itself through a closed-loop optimization cycle that employs independent reviewer agents to prevent anchoring effects and a ratchet-based version control mechanism that automatically reverts changes if they fail to improve baseline scores. It also features exploratory structural rewriting to overcome

Rivet is a visual LLM workflow designer and AI agent orchestration engine. It serves as a development environment for building retrieval augmented generation pipelines and a TypeScript library for embedding visual AI graphs and prompt logic into JavaScript applications. The system differentiates itself through a node-based editor that maps data flow between language models, vector databases, and external APIs. It provides specialized tools for prompt engineering, including interfaces for iterative prompt refinement and A/B testing to improve model response quality. The platform covers a broa

WizardLM is a large language model and instruction-tuning framework designed to execute sophisticated coding, mathematical, and conversational tasks. It functions as an AI system for mathematical reasoning and code generation, as well as a synthetic dataset generator used to train other language models. The project is distinguished by its evolutionary instruction tuning, which uses a method to rewrite simple instructions into complex tasks. This process expands training dataset difficulty and produces a high volume of open-domain tasks across various difficulty levels. The system covers capa

This project provides a structured methodology and framework for engineering, testing, and maintaining system instructions for large language models. It serves as a guide for designing clear, explicit prompts that ensure models follow complex tasks with consistent accuracy. The framework distinguishes itself by treating prompt development as a rigorous engineering process, emphasizing the use of version control to manage prompt templates and style guides. By organizing instructions into modular, reusable components, it facilitates long-term maintainability and reduces technical debt within de

Helicone is an AI gateway and observability platform designed to intercept, manage, and monitor interactions with large language models. By acting as a reverse-proxy, it provides a centralized layer for routing requests across multiple AI providers, allowing developers to maintain consistent application logic while gaining deep visibility into model performance, usage, and costs. The platform distinguishes itself through a robust suite of traffic management and prompt engineering tools. It enables policy-driven control, including automatic failover between providers, rate limiting, and edge-b

TensorZero is an inference gateway and experimentation framework designed to manage the lifecycle of large language models in production environments. It functions as a central proxy that routes requests across multiple artificial intelligence providers while providing the infrastructure necessary to monitor performance, track costs, and ensure service reliability. The platform distinguishes itself by integrating a comprehensive evaluation engine and an observability pipeline directly into the request flow. It enables developers to conduct controlled experiments and A/B tests to compare diffe

This project provides a framework for managing multi-agent systems, designed to automate complex software development, infrastructure, and business workflows. It functions as a multi-agent workflow orchestrator that routes tasks to domain-specific workers while maintaining state persistence and infrastructure automation. By leveraging large language models, the system decomposes high-level objectives into actionable plans, ensuring that complex operations are executed with consistency and reliability. The framework distinguishes itself through its hierarchical agent registry and policy-driven

This project serves as an educational resource and guide for prompt engineering, providing a structured methodology for interacting with large language models. It focuses on teaching core strategies to improve the reliability, accuracy, and consistency of model outputs across a variety of natural language processing tasks. The framework emphasizes the use of standardized templates and logical decomposition to manage complex instructions. By implementing techniques such as few-shot context injection, iterative refinement, and delimiter-based segmentation, the project demonstrates how to guide

This repository is a collection of specialized toolsets and libraries for large language model prompt engineering and security testing. It provides a library of advanced templates and frameworks designed to optimize the quality and specificity of model responses. The project includes resources for red teaming and security research, featuring a repository of prompts designed to bypass safety filters and operational constraints. It also provides techniques for system prompt extraction to reveal the internal instructions and configurations of AI personas. The collection covers a broader surface

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,

Promptify is a suite of tools designed for model evaluation, prompt management, token cost tracking, structured extraction, and unified API gateway access. It provides a standardized interface to manage requests and responses across multiple large language model providers. The project features a prompt management platform for engineering and versioning prompts with structured output validation. It includes a dedicated evaluation framework to measure model performance using precision, recall, and f1 scores against labeled datasets, alongside a token cost tracker to monitor the financial expens

ChatGPT-Shortcut is a prompt engineering toolkit and management library designed to organize, refine, and deploy structured instructions for large language models. It functions as a browser-based prompt injector and a self-hosted prompt database, allowing users to maintain a curated collection of specialized templates. The project features a community prompt gallery where users can publish, discover, and vote on effective templates. It distinguishes itself by integrating these libraries directly into chat interfaces via userscripts or browser extensions, enabling access to prompts through sid

Kiln is an LLM development workbench and evaluation framework designed for designing, testing, and optimizing prompts and AI agents. It functions as a multi-agent orchestrator and a RAG optimization tool, providing a visual interface for the iterative development of AI systems. The project distinguishes itself through a comprehensive fine-tuning pipeline that supports zero-code model training and reasoning distillation. It enables the creation of hierarchical multi-agent systems where specialized actors coordinate via tool calling, and it implements a Model Context Protocol server to expose t

This project is a comprehensive guide and framework for large language model prompt engineering. It provides a collection of techniques and patterns for optimizing model responses through structured system prompts, context management, and a variety of implementation patterns. The project focuses on several specialized domains, including the creation of autonomous agents through reasoning loops and the implementation of retrieval augmented generation to inject semantic context into prompts. It also provides methods for enforcing structured outputs in serialization formats like JSON or YAML for

Agenta is a Prompt Ops lifecycle manager and prompt management platform that decouples prompt engineering from application code. It serves as a centralized system for developing, versioning, and deploying prompt templates and model configurations across different environments. The platform functions as an AI agent orchestrator with a visual interface for building agent workflows and connecting models to external tools. It further acts as an evaluation framework and observability tool, utilizing OpenTelemetry to capture execution traces, monitor latency, and track token costs. The system cove

Ragas is an evaluation framework designed to measure the performance of retrieval-augmented generation pipelines and autonomous agent workflows. It provides a comprehensive suite of tools for benchmarking system outputs, utilizing language models as automated judges to score performance against defined rubrics and reference data. By standardizing inputs, retrieved contexts, and generated responses into a unified schema, the project enables consistent analysis across complex AI applications. The framework distinguishes itself through its ability to generate synthetic test datasets from existin

The synthetic data kit is an integrated framework designed to generate, curate, and format training datasets for language models. It provides an end-to-end pipeline that transforms raw source documents into structured data suitable for fine-tuning, reasoning, and tool-use model training. The framework distinguishes itself through a modular orchestration engine that manages the entire lifecycle of data preparation. It supports multimodal input by extracting both text and image content from various file formats, while employing context-aware chunking to maintain semantic coherence. The generati

Prompt Master is an AI skill that automates prompt engineering by detecting the target AI system and applying the correct prompt architecture automatically. It generates optimized prompts for over 30 different AI tools, adapting format and syntax to each target system without requiring manual conversion. The system distinguishes itself through several integrated capabilities. It extracts missing dimensions of intent from vague requests by asking up to three targeted clarifying questions before generating a final prompt. A memory block of prior decisions and constraints is prepended to maintai

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

AirSim is a high-fidelity simulation platform designed for the development and testing of autonomous vehicles. Built as a plugin for game engines, it provides a physics-based environment that models vehicle dynamics and sensor data, serving as a foundation for robotics research, computer vision training, and reinforcement learning. The platform distinguishes itself through its support for hardware-in-the-loop and software-in-the-loop testing, allowing developers to validate control logic and firmware against real-world signals or concurrent processes. It offers extensive programmatic control

Faker is a library for generating synthetic data and mock information to populate development and testing environments. It provides a structured way to create realistic values such as names, addresses, and dates, allowing developers to validate application logic and visualize user interfaces without relying on production data. The library distinguishes itself through its support for deterministic generation, which uses fixed seeds to ensure that data sequences remain identical across multiple test executions. It also features a modular architecture that separates generation logic into indepen