Prompt engineering guides

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 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

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 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

Learning-Prompt is a collection of educational resources and step-by-step guides designed for mastering large language model interaction and text-to-image tools. It provides a guided course on prompt engineering for large language models alongside tutorials for creating visual content with generative AI. The project utilizes a curated curriculum that organizes material into sequential lessons and modular tracks. Instruction is delivered through step-by-step tutorials and an iterative framework of drafting, testing, and refining prompts, using side-by-side comparisons of raw and optimized exam

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

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

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

llm-universe is a structured learning resource and technical guide focused on the development of large language model applications. It serves as a curriculum for mastering model orchestration, the creation of autonomous conversational agents, and the implementation of retrieval-augmented generation systems. The project provides detailed instructions on connecting model APIs with memory and tools to create execution chains. It specifically covers the construction of retrieval pipelines, including the process of cleaning raw documents, generating embeddings, and integrating vector databases to

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

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

Promptflow is a development framework and orchestrator for building applications powered by large language models. It functions as a suite of tools for designing, orchestrating, and deploying AI workflows by linking prompts, custom Python code, and language models into executable sequences. The project is distinguished by a visual AI workflow designer that allows for the creation of directed acyclic graphs of logic nodes. It provides a dedicated prompt engineering environment for versioning and comparing templates, alongside stateful execution tracing to record function calls and variable val

JARVIS is a system for large language model task orchestration, deployment management, and automation benchmarking. It utilizes a task orchestrator to decompose complex requests into actionable steps and coordinates various expert models to synthesize final responses. The project includes an AI model deployment manager to handle the local deployment of expert models across different hardware scales. It further provides an AI workflow API consisting of web endpoints used to trigger automated task workflows and retrieve results from model selection stages. The framework incorporates an automat

This project is a community-driven library of structured text inputs designed to guide large language models into specific roles, behaviors, and operational modes. It functions as a comprehensive repository of prompt engineering resources, providing reusable templates that allow users to override default model tendencies and enforce domain-specific response patterns through instruction-following logic. The collection distinguishes itself by offering specialized persona-based directives that constrain model output to simulate professional experts or functional technical environments. By utiliz

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

This project is a community-driven knowledge repository and technical learning resource focused on the field of generative artificial intelligence. It serves as a centralized hub for developers and practitioners to access curated research, tutorials, and foundational concepts necessary for building and deploying modern artificial intelligence applications. The platform distinguishes itself through a collaborative, distributed contribution model that aggregates diverse learning materials into a structured, searchable knowledge base. It covers a wide range of specialized topics, including retri

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

PurpleLlama is a collection of security toolsets and frameworks designed to audit large language model vulnerabilities and implement runtime input-output guardrails. It provides a security evaluation framework and benchmark suite to quantify risks associated with prompt injections and the generation of malicious code. The project includes a content moderator and input-output filters that use a standardized taxonomy to identify and block harmful content, jailbreaking attempts, and insecure commands. It also features capabilities for sensitive document classification to prevent the unauthorized

This project is a centralized repository for the collection and analysis of system instructions and behavioral configurations extracted from large language models and AI-powered software. It serves as a research archive that documents the internal directives, operational constraints, and safety protocols that define how various artificial intelligence agents interact with users. The repository distinguishes itself through a crowdsourced approach to data aggregation, maintaining a historical record of configuration changes across a wide range of proprietary models and coding assistants. By org

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

BlackFriday-GPTs-Prompts is a curated repository of specialized text instructions and configuration templates designed to guide large language models through complex tasks. It functions as an open-source resource for users seeking to perform advanced artificial intelligence operations without relying on paid subscription services or external cloud processing. The project distinguishes itself by providing structured prompt-based logic that enables users to execute AI-driven workflows directly within their existing environments. By utilizing these pre-configured templates, users can automate re

RagaAI-Catalyst is a suite of software implementation tools providing an SDK, dashboard, and platform for monitoring, debugging, red-teaming, and evaluating agentic AI workflows. It serves as an observability framework for tracing the execution paths of large language models and multi-agent systems. The project distinguishes itself through a security suite for automated red-teaming and vulnerability scanning to detect biases, alongside a centralized prompt registry that decouples templates from application code. It further provides an evaluation platform that combines synthetic data generatio

Giskard is an evaluation framework, testing library, and quality monitoring system for large language models and AI agents. It serves as a toolkit for quantifying model performance and reliability, providing specialized capabilities for validating retrieval-augmented generation pipelines. The project distinguishes itself through an automated red teaming tool and security scanner designed to identify vulnerabilities, prompt injections, and safety risks. It utilizes adversarial probing and synthetic edge case generation to quantify model robustness and detect information disclosure. The platfo

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 technical learning resource and developer knowledge base focused on the integration of large language models into software applications. It provides a structured collection of guides and code examples designed to teach developers how to implement intelligent features using proven patterns and best practices. The repository distinguishes itself through a library of functional demonstrations that cover complex topics such as retrieval-augmented generation, function calling, and prompt engineering workflows. These materials are organized into a modular structure, allowing for t

A curated list of awesome resources, tools, and other shiny things for LLM prompt engineering.

🔥 Comprehensive survey on Context Engineering: from prompt engineering to production-grade AI systems. hundreds of papers, frameworks, and implementation guides for LLMs and AI agents.

This platform serves as a centralized management system for organizing, refining, and versioning AI instructions and agent skills. It functions as a repository that enables users to store, categorize, and retrieve structured prompts, ensuring consistent performance across various artificial intelligence models. By integrating with the Model Context Protocol, the system allows external AI assistants and development environments to discover and access these instruction libraries directly. The platform distinguishes itself through its focus on prompt engineering and automated refinement, utilizi

Promptfoo is an evaluation framework designed for testing, benchmarking, and red-teaming language models and agentic workflows. It provides a unified environment to run prompts against multiple providers, allowing developers to systematically validate model outputs against objective assertions, semantic similarity metrics, and custom grading rubrics. The platform distinguishes itself through a provider-agnostic execution layer and a stateful orchestrator capable of simulating multi-turn conversations and complex tool-use trajectories. It includes a dedicated adversarial mutation pipeline that

This project is a reasoning framework and agent orchestrator that implements the Tree of Thoughts methodology to improve the logical output of large language models. It functions as a search-based problem solver, representing complex tasks as a state-space branching model where discrete thoughts serve as nodes and logical transitions serve as edges. The system coordinates multiple model agents to generate, evaluate, and prune candidate solutions. It employs depth-first search heuristics and recursive evaluation to explore multiple reasoning paths, filtering out low-quality branches to iterate

Automatic Prompt Engineer is a framework designed to automate the generation, refinement, and performance measurement of language model instructions. It functions as a systematic tool for optimizing prompt phrasing by iteratively testing candidate instructions against specific input and output datasets to maximize task accuracy. The system distinguishes itself through an evaluation-driven approach that uses automated feedback loops to score prompt variations. By employing template-based input structuring, it ensures consistent testing environments where candidate instructions are measured aga

L1B3RT4S is an adversarial machine learning toolkit designed for red teaming and evaluating the robustness of large language models. It provides a research framework for investigating how safety alignment mechanisms and content moderation systems respond to sophisticated input strategies. The project focuses on identifying vulnerabilities in model guardrails by employing techniques such as adversarial narrative framing, dynamic context injection, and latent space steering. It utilizes multi-agent prompt decomposition and recursive text transformation to analyze how structural changes to input

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

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

This project is a comprehensive AI infrastructure that combines an LLM agent orchestration framework, an autonomous research system, and a local AI environment. It centers on the creation of a personal knowledge graph and a programmatic prompt engineering library to provide long-term memory and optimized reasoning for artificial intelligence tasks. The system is distinguished by its ability to compose multi-agent teams using specialized personas and deterministic skills to execute complex workflows. It features an autonomous research pipeline capable of deep investigations and adversarial ana

Fabric is a command-line orchestrator designed to automate complex data processing and content generation tasks by chaining artificial intelligence models with modular prompt templates. It functions as a terminal-based tool that utilizes standard input and output streams, allowing users to pipe data directly into predefined reasoning strategies. By providing a model-agnostic abstraction layer, the system decouples execution logic from specific artificial intelligence vendors, normalizing requests and responses across different service providers. The platform distinguishes itself through its p

Outlines is a guided text generation framework and structured output engine for large language models. It enforces precise structural constraints on model output during the sampling process to ensure the generation of valid data. The framework ensures that model outputs strictly adhere to predefined data models, including JSON schemas, regular expressions, and formal grammars. This enables the conversion of natural language inputs into structured arguments for function calling and the generation of valid JSON for downstream processing. The system manages model orchestration through prompt te

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

Arize Phoenix is an LLM observability platform and evaluation framework designed to capture execution traces and monitor large language model applications. It serves as a prompt management system for versioning and testing templates, and as a self-hosted AI operations infrastructure for managing telemetry and experiments. The platform differentiates itself through a specialized embedding visualization tool used to detect data drift and optimize vector search. It provides a comprehensive evaluation suite that utilizes judge-based evaluators and ground-truth datasets to score model outputs, and

LangChain is an orchestration framework designed for building, managing, and deploying applications powered by large language models. It provides a unified integration layer that normalizes disparate model provider APIs into a consistent set of primitives, enabling developers to build complex, multi-step AI workflows that manage state, memory, and tool execution. The project distinguishes itself through a durable execution runtime that maintains persistent state across long-running processes by checkpointing progress to external storage. It models agent workflows as directed graphs, allowing

LlamaIndex is a comprehensive development framework designed to connect private or external data sources to large language models. It functions as a data-centric toolkit that enables the construction of retrieval-augmented generation systems, allowing developers to build applications that provide context-aware answers based on specific organizational information. The project distinguishes itself through a robust agentic orchestration engine that supports the creation of autonomous agents capable of multi-step reasoning, memory management, and complex tool execution. Beyond simple retrieval, 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-

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

This project is an interactive educational resource designed to teach prompt engineering for large language models. It provides a structured curriculum that guides users through the process of structuring instructions and context to improve the accuracy and consistency of model outputs. The platform functions as an interaction sandbox, allowing users to experiment with various prompting strategies directly within their browser. By executing tutorial examples in this environment, users receive immediate feedback on their inputs, facilitating the prototyping of prompts for integration into soft

This project is a community-driven knowledgebase and registry for AI agent configurations. It serves as a centralized repository for system prompts, environment settings, and integration strategies designed to standardize the behavior of various AI-assisted development tools. By capturing these configurations in a structured format, the project enables developers to maintain consistent AI agent performance across different workstations and environments. The repository distinguishes itself through a hierarchical, version-controlled architecture that treats prompt engineering patterns as portab

This project is a collection of educational resources and curricula designed for mastering AI pair programming and prompt engineering. It provides a structured training course and instructional materials for integrating AI assistants into the software development lifecycle. The materials cover the use of large language models to modernize legacy code and translate applications between programming languages. It includes a specific guide for crafting natural language queries to generate code and automate development workflows. The content addresses a broad range of capabilities, including AI-a

Instructor is a library designed to parse, validate, and map unstructured language model responses into strongly typed, schema-compliant data objects. It provides a framework for structured data extraction that uses data modeling classes to enforce strict type constraints on model outputs, ensuring that generated content consistently matches expected structures. The library distinguishes itself through an automated error recovery system that manages the lifecycle of failed extraction attempts. When a model output fails to meet defined schema requirements, the framework automatically triggers

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

Python package for easily interfacing with chat apps, with robust features and minimal code complexity.