# Prompt engineering and LLM app dev

> AI-ranked search results for `Prompt engineering and LLM app dev` on awesome-repositories.com — ordered by an LLM for relevance, best match first. 104 total matches; showing the top 30.

Explore on the web: https://awesome-repositories.com/q/prompt-engineering-and-llm-app-dev

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## Results

- [dair-ai/prompt-engineering-guide](https://awesome-repositories.com/repository/dair-ai-prompt-engineering-guide.md) (75,678 ⭐) — 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
- [anthropics/prompt-eng-interactive-tutorial](https://awesome-repositories.com/repository/anthropics-prompt-eng-interactive-tutorial.md) (36,423 ⭐) — 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
- [nirdiamant/prompt_engineering](https://awesome-repositories.com/repository/nirdiamant-prompt-engineering.md) (7,159 ⭐) — 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,
- [plexpt/awesome-chatgpt-prompts-zh](https://awesome-repositories.com/repository/plexpt-awesome-chatgpt-prompts-zh.md) (60,656 ⭐) — 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
- [brexhq/prompt-engineering](https://awesome-repositories.com/repository/brexhq-prompt-engineering.md) (9,538 ⭐) — 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
- [x1xhlol/system-prompts-and-models-of-ai-tools](https://awesome-repositories.com/repository/x1xhlol-system-prompts-and-models-of-ai-tools.md) (141,061 ⭐) — 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
- [willwulfken/midjourney-styles-and-keywords-reference](https://awesome-repositories.com/repository/willwulfken-midjourney-styles-and-keywords-reference.md) (12,285 ⭐) — 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
- [danielmiessler/fabric](https://awesome-repositories.com/repository/danielmiessler-fabric.md) (42,408 ⭐) — 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
- [f/awesome-chatgpt-prompts](https://awesome-repositories.com/repository/f-awesome-chatgpt-prompts.md) (163,835 ⭐) — 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
- [stanfordnlp/dspy](https://awesome-repositories.com/repository/stanfordnlp-dspy.md) (35,325 ⭐) — 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-
- [datawhalechina/llm-cookbook](https://awesome-repositories.com/repository/datawhalechina-llm-cookbook.md) (24,263 ⭐) — 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
- [chatgptnextweb/nextchat](https://awesome-repositories.com/repository/chatgptnextweb-nextchat.md) (88,256 ⭐) — NextChat is a self-hosted web application that provides a unified interface for interacting with multiple large language models. It functions as a conversational platform where users can manage and switch between diverse AI providers through configurable API backends, maintaining full control over their data and infrastructure.

The platform features a persistent session layer designed to handle long-running dialogues by managing message history and context. It distinguishes itself through a structured prompt engineering environment that allows for the development and application of templates
- [chiphuyen/aie-book](https://awesome-repositories.com/repository/chiphuyen-aie-book.md) (13,779 ⭐) — 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
- [asgeirtj/system_prompts_leaks](https://awesome-repositories.com/repository/asgeirtj-system-prompts-leaks.md) (42,673 ⭐) — 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
- [antonosika/gpt-engineer](https://awesome-repositories.com/repository/antonosika-gpt-engineer.md) (55,200 ⭐) — GPT-Engineer is an autonomous agent and framework designed for AI-assisted software development. It functions as a generative codebase architect that translates natural language requirements into complete, functional software projects by reading and writing files directly to the local file system.

The platform distinguishes itself through an agentic workflow orchestrator that sequences complex programming tasks into manageable, iterative steps. It supports multi-modal input processing, allowing users to incorporate visual data like screenshots or diagrams to guide UI generation. Furthermore,
- [datawhalechina/prompt-engineering-for-developers](https://awesome-repositories.com/repository/datawhalechina-prompt-engineering-for-developers.md) (24,267 ⭐) — 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
- [tatsu-lab/stanford_alpaca](https://awesome-repositories.com/repository/tatsu-lab-stanford-alpaca.md) (30,266 ⭐) — This project provides an end-to-end framework for adapting large language models to follow user instructions through supervised fine-tuning. It functions as a comprehensive training pipeline that enables the creation of specialized assistant models by minimizing the difference between predicted outputs and target responses within structured instruction datasets.

The framework distinguishes itself by integrating synthetic data generation with memory-efficient training techniques. It utilizes powerful language models to iteratively expand small sets of human-written seeds into diverse, high-qua
- [linshenkx/prompt-optimizer](https://awesome-repositories.com/repository/linshenkx-prompt-optimizer.md) (30,927 ⭐) — Prompt Optimizer is a framework designed for the iterative refinement and testing of text-based instructions for large language models. It functions as an automated evaluation pipeline that systematically adjusts prompt structure, constraints, and clarity to improve the accuracy and consistency of model outputs.

The system distinguishes itself through a model-agnostic interface that standardizes communication across different artificial intelligence providers. It incorporates a versioned asset management system to track prompt history, enabling developers to maintain consistency and perform r
- [anthropics/claude-cookbooks](https://awesome-repositories.com/repository/anthropics-claude-cookbooks.md) (45,835 ⭐) — 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
- [disler/claude-code-hooks-mastery](https://awesome-repositories.com/repository/disler-claude-code-hooks-mastery.md) (3,090 ⭐) — This project is an extension framework and orchestration system for Claude Code that uses lifecycle hooks to intercept and augment the execution flow of agents. It serves as a management layer for automating session startup, handling project context loading, and performing cleanup routines during AI coding sessions.

The framework provides an orchestration system to spawn and manage specialized sub-agents with distinct prompts and toolsets to decompose complex technical tasks. It functions as a prompt engineering middleware to validate and inject context into user requests and as a tool guardr
- [pythagora-io/gpt-pilot](https://awesome-repositories.com/repository/pythagora-io-gpt-pilot.md) (33,743 ⭐) — GPT-Pilot is an autonomous development tool designed to build, debug, and manage entire software projects. It functions as an AI-powered coding assistant that translates high-level natural language requirements into structured file architectures and functional source code. By acting as an autonomous software engineer, the system automates the software development lifecycle, from initial boilerplate creation to the implementation of complex logic.

The project distinguishes itself through a recursive task decomposition process that breaks complex requirements into manageable steps, which are th
- [guidance-ai/guidance](https://awesome-repositories.com/repository/guidance-ai-guidance.md) (21,502 ⭐) — 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
- [shubhamsaboo/awesome-llm-apps](https://awesome-repositories.com/repository/shubhamsaboo-awesome-llm-apps.md) (114,725 ⭐) — This repository serves as a comprehensive collection of resources, templates, and starter code for building artificial intelligence applications. It provides a centralized hub for developers to access practical implementations of common workflows, including retrieval-augmented generation pipelines and autonomous agent loops, alongside educational materials designed to support rapid prototyping and experimentation.

The project distinguishes itself by offering a dual focus on technical implementation and critical analysis. It provides a library of lightweight, single-file agents and tutorials f
- [atuinsh/atuin](https://awesome-repositories.com/repository/atuinsh-atuin.md) (30,266 ⭐) — Atuin is a command-line tool that replaces standard shell history with a searchable, encrypted SQLite database. By hooking into shell initialization scripts, it provides an interactive, keyboard-driven interface for real-time command filtering and retrieval. The platform ensures data privacy through a client-side encryption layer, securing sensitive history and configuration data before it is synchronized across multiple machines.

Beyond history management, Atuin functions as an executable documentation platform that enables teams to create and share interactive runbooks. These documents use
- [lllyasviel/fooocus](https://awesome-repositories.com/repository/lllyasviel-fooocus.md) (50,260 ⭐) — Fooocus is a generative image interface designed to simplify the creation of high-quality visual content from text descriptions. It functions as a latent diffusion pipeline and model orchestrator, managing the complex interactions between neural network layers, mathematical samplers, and hardware resource allocation to produce professional-grade imagery.

The project distinguishes itself through a sophisticated prompt engineering engine and modular style management. Users can dynamically modify output characteristics by injecting style adapters directly into prompts or by utilizing wildcards a
- [mattpocock/skills](https://awesome-repositories.com/repository/mattpocock-skills.md) (131,422 ⭐) — This project is an AI agent workflow framework and development toolkit designed for AI-driven software engineering. It provides a system of modular instructions, prompt libraries, and standardized routines to orchestrate complex engineering sequences and automate the decomposition of plans into technical tasks.

The system differentiates itself through advanced context management and prompt engineering, using state compression and handoff documents to preserve conversation history between different AI sessions. It employs a structured library of prompt skills and high-signal trigger words to e
- [aider-ai/aider](https://awesome-repositories.com/repository/aider-ai-aider.md) (46,305 ⭐) — Aider is a command-line interface tool that enables large language models to directly edit, refactor, and manage source code within a local repository. It functions as an AI-powered coding assistant that integrates into the developer workflow, allowing users to apply code changes through natural language prompts while maintaining repository context and version control.

The tool distinguishes itself through a specialized diff-based patching engine that parses model-generated search-and-replace blocks to modify specific file segments without rewriting entire files. It features a provider-agnost
- [acheong08/edgegpt](https://awesome-repositories.com/repository/acheong08-edgegpt.md) (7,873 ⭐) — EdgeGPT is a reverse engineered API wrapper and programmatic client for interacting with Bing Chat and associated large language model services. It enables the retrieval of text responses, code snippets, and suggested questions through a structured interface.

The project uses exported browser cookies for authentication and implements an automated session rotation system to bypass daily request limits and regional restrictions. It manages multiple cookie sets to maintain continuous service uptime.

The system also includes capabilities for AI image generation, automating requests to create vis
- [affaan-m/everything-claude-code](https://awesome-repositories.com/repository/affaan-m-everything-claude-code.md) (216,499 ⭐) — Everything Claude Code is an agentic framework designed to orchestrate complex software development workflows through specialized subagent delegation. It functions as a control plane that manages agent behavior, tool access, and context window efficiency, allowing developers to break down large tasks into focused, scoped sub-processes that prevent system overload.

The framework distinguishes itself through a robust security and automation layer that includes automated static analysis and adversarial red-teaming to audit agent configurations. It enables the creation of reusable behavioral patt
- [boundaryml/baml](https://awesome-repositories.com/repository/boundaryml-baml.md) (7,636 ⭐) — BAML is a prompt engineering framework and LLM client generator that defines AI prompts as type-safe functions. It serves as a structured data extraction tool and workflow orchestrator, transforming unstructured model responses into strongly typed objects using a custom schema language and alignment algorithms.

The project distinguishes itself by using a compiler to generate language-specific boilerplate code for API communication and output parsing. It features a dedicated environment for designing complex prompt templates with conditional logic and reusable snippets, and employs genetic alg