Projects sharing features with Prompt Engineering Guide

This project is a curated library of community-driven prompt templates and personas designed to improve interactions with large language models. It functions as a prompt engineering guide, providing interactive tutorials and examples to teach advanced design and reasoning techniques. The library can operate as a Model Context Protocol server, providing a standardized interface for AI tools and agents to access prompt data as a service. For organizations, it offers a self-hosted repository option that allows for private deployment on internal infrastructure with custom authentication and data

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 repository serves as an educational resource and technical guide for developers learning to integrate large language models into software applications. It provides practical lessons and code examples focused on building systems that perform automated text generation, data analysis, and interactive chat tasks. The project functions as a framework for understanding how to connect applications to external artificial intelligence services. It covers the implementation of secure authentication, the orchestration of network requests, and the configuration of model parameters such as temperatur

This project is a technical curriculum and development guide focused on large language model prompt engineering, fine-tuning, and the creation of retrieval augmented generation applications. It serves as a comprehensive resource for developers to master crafting precise instructions and textual patterns to improve the quality and predictability of model outputs. The material covers the end-to-end workflow of adapting open-source models to specific datasets and integrating language models with vector databases to generate responses based on private information. It also provides a systematic ap

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

Context-Engineering is a prompt engineering framework and cognitive architecture for large language models. It provides a set of patterns and methodologies for designing structured prompts and modular reasoning flows that decompose complex tasks into specialized, step-by-step problem solving templates. The project distinguishes itself through stateful prompt management and context window optimization. It maintains persistent memory across multiple interaction turns by compressing conversation history into compact internal state cells and employs techniques to maximize information density per

This project serves as a comprehensive, curated directory of resources, tools, and platforms dedicated to the generative artificial intelligence ecosystem. It functions as a central hub for developers and researchers to discover the frameworks, models, and services necessary for building, deploying, and managing intelligent software applications. The directory distinguishes itself by providing a structured index of specialized tooling across several technical domains. It covers the full lifecycle of generative AI, including the development of autonomous agent systems, the implementation of re

Qwen-Agent is a development framework for building autonomous software applications that leverage large language models to plan, reason, and execute complex tasks. It functions as an orchestration engine that enables models to interact with external APIs, manage persistent memory, and maintain context across multi-step workflows. The framework distinguishes itself through a multi-agent collaboration platform that allows independent agent instances to exchange structured messages and delegate sub-tasks to one another. By utilizing iterative reasoning loops and dynamic prompt injection, the sys

Guidance is a generative AI orchestration framework designed to manage complex interactions with language models by embedding programmatic control directly into the prompt generation process. It functions as a prompt programming environment that allows developers to interleave raw text with executable logic, enabling the construction of sophisticated, multi-step agentic workflows. The framework distinguishes itself through grammar-constrained token sampling and stateful stream interception, which restrict the model's output distribution based on formal language rules. By enforcing these const

LangChain is a framework for building applications that chain large language models with external data sources and third-party tools. It serves as an orchestrator for autonomous agents that use language models to plan and execute multi-step tasks, while providing a toolkit for linking interoperable AI components into sequences to prototype complex model behaviors. The project provides a model agnostic integration layer, allowing users to switch between different language model providers using a standardized interface. It also includes tools for observability and evaluation to track the perfor

This project is a comprehensive, open-source educational curriculum designed to guide developers through the mastery of generative artificial intelligence. It provides a structured learning path that covers foundational concepts, prompt engineering, and the practical application of large language models. The repository serves as a central hub for skill acquisition, offering sequential modules that progress from basic model mechanics to advanced architectural patterns. The curriculum distinguishes itself by focusing on the end-to-end lifecycle of intelligent software, including the implementat

LMOps is a research-driven operations framework for optimizing the deployment, fine-tuning, and performance of large language models. It provides a specialized toolkit for foundation model adaptation, inference acceleration, prompt optimization, and context orchestration. The framework distinguishes itself through an inference accelerator that reduces token generation latency by verifying and copying overlapping text spans from reference documents. It also features a prompt engineering optimizer that employs reinforcement learning, beam search, and non-natural language markers to automaticall

A curated list of practical guide resources of LLMs (LLMs Tree, Examples, Papers)

This repository is a collection of guides, notebooks, and recipes for implementing advanced prompting techniques and workflow patterns with large language models. It serves as a prompt engineering guide, an evaluation suite for scoring prompt quality, and a framework for orchestrating agents and integrating external tools. The project provides implementation patterns for building applications with Claude, specifically focusing on coordinating multiple models to split complex tasks between high-reasoning and high-efficiency agents. It includes technical demonstrations for multimodal data proce

This project provides methodologies and guides for structured prompt engineering, generative workflows, and specialized image generation strategies. It serves as a framework for optimizing inputs to large language models across coding, writing, and analysis tasks, as well as a library of techniques for controlling diffusion models. The project distinguishes itself through an AI-driven software design framework that converts business requirements into technical architectures and code using domain-driven prompting. It also implements generative AI workflow patterns that use sequential prompt pi

This project serves as a comprehensive reference tool for prompt engineering within generative image models. It provides a structured guide for exploring artistic styles, technical parameters, and keyword combinations to assist in achieving specific aesthetic outcomes and consistent visual themes. The resource distinguishes itself by enabling direct comparisons between different model versions, allowing users to observe how specific keywords and settings influence output quality over time. By organizing visual examples and technical data into a hierarchical taxonomy, it facilitates the iterat

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,

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

This project is a collection of tutorials and guides for building large language model applications using the LangChain framework, written in Chinese. It serves as a learning resource for developing software that integrates language models with memory and chain-based logic. The resource provides specific walkthroughs for implementing retrieval augmented generation systems using vector stores and document loaders. It includes guides on creating autonomous agents that dynamically select and execute external tools, as well as tutorials for translating plain text queries into executable database

This project serves as a community-driven educational resource focused on prompt engineering for large language models. It provides structured learning materials designed to help developers integrate artificial intelligence tools into their software workflows and optimize the quality of model outputs. The repository distinguishes itself through a comprehensive localization effort, offering translated technical documentation and multilingual subtitle files for instructional video content. By utilizing a collaborative, version-controlled workflow, the project ensures that complex engineering co

This repository serves as a comprehensive library of architectural blueprints and code examples for integrating large language models into software applications. It functions as a developer learning resource, providing structured tutorials and implementation patterns that demonstrate how to build intelligent features using advanced prompting and data processing techniques. The collection distinguishes itself by focusing on complex reasoning and data-grounding workflows. It provides practical guidance on implementing retrieval-augmented generation pipelines, which connect language models to pr

This project is a comprehensive educational curriculum and structured learning path covering the full lifecycle of large language models. It provides a guided progression through the theory, architecture, training, and deployment of these models. The curriculum includes specialized guides on transformer architecture, model training tutorials, and frameworks for designing autonomous agents. It also provides dedicated resources for studying model safety and ethics. The material covers a wide range of technical capabilities, including distributed training strategies, parameter-efficient fine-tu

SuperPrompt is an AI agent prompting tool and meta-prompting system designed to engineer complex prompts that enable autonomous behaviors and advanced reasoning in large language models. It functions as a framework for creating structured instructions and notations that guide models through multi-step tasks and autonomous workflows. The system utilizes a structured prompt library featuring XML notations and holographic metadata to force models into deeper thought patterns and novel perspectives. It employs dynamic meta-prompting to automatically rewrite operational constraints and objectives

This project serves as a comprehensive educational resource and technical handbook for engineers building applications powered by large language models. It provides a structured framework for mastering the principles of artificial intelligence engineering, covering the full lifecycle of model development from initial design to production deployment. The repository distinguishes itself by offering a deep dive into the practical implementation of advanced design patterns, including retrieval-augmented generation, agentic tool orchestration, and parameter-efficient model adaptation. It emphasize

Promptbase is a prompt engineering framework designed for designing, testing, and optimizing prompts for large language models. It provides a system for measuring model accuracy and performance through an evaluation toolkit that compares outputs against ground-truth datasets. The project also includes an orchestration pipeline for automating multi-component machine learning tasks across cloud-based endpoints and a utility for preparing retrieval-augmented generation datasets. The framework distinguishes itself through advanced response quality optimization, utilizing chain-of-thought generato

This project is a collection of architectural templates and design patterns for building autonomous AI agents. It provides a framework for transitioning from simple prompt-response loops to goal-oriented systems that utilize structural patterns to increase autonomy and improve the reliability of complex task completion. The framework focuses on reasoning orchestration, specifically through the implementation of reflection and self-correction cycles. It enables the coordination of specialized agents via task delegation and state sharing to solve complex problems. The architectural surface cov

Kotaemon is an orchestration framework designed for building modular, agentic workflows that integrate document processing, retrieval-augmented generation, and multi-step reasoning. It provides a comprehensive platform for developing document-based question answering systems, allowing users to chain language models, prompt templates, and external tools into complex, automated pipelines. The system distinguishes itself through a highly modular architecture that emphasizes component-based composition and schema-driven data exchange. It supports autonomous agents capable of decomposing complex q