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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 main features of thinkingjimmy/learning-prompt are: Prompt Engineering Guides, AI Image Generators, Text-to-Image Generators, Interactive AI Conversations, Prompt Engineering Techniques, Interaction Optimization, Generative AI Skill Paths, AI Image Generation.
Projects with overlapping indexed features include: trigaten/learn_prompting — Learn_Prompting is an educational project focused on prompt engineering, providing the principles and techniques… latentcat/qrbtf — qrbtf is an AI QR code generator and image synthesis system that blends machine-readable data with artistic imagery.… divamgupta/diffusionbee-stable-diffusion-ui — DiffusionBee is a Stable Diffusion desktop client for macOS that functions as an AI image generator and editor. It… pandabearlab/prompt-tutorial — This project serves as an educational resource and guide for prompt engineering, providing a structured methodology… sygil-dev/sygil-webui — Sygil-webui is a web interface for Stable Diffusion latent diffusion models, providing a creative suite for… youmind-openlab/awesome-nano-banana-pro-prompts — This project is a comprehensive generative AI prompt library and image generation toolkit designed to streamline the…
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
qrbtf is an AI QR code generator and image synthesis system that blends machine-readable data with artistic imagery. It uses a latent diffusion model and spatial control networks to produce functional QR codes that incorporate visual art generated from descriptive text prompts. The system provides a dedicated interface and programmatic API for tuning visual output, allowing for the adjustment of control strength, padding ratios, and error correction levels. It supports deterministic sampling via random seeds and the use of negative prompts to refine the final aesthetic of the generated assets
DiffusionBee is a Stable Diffusion desktop client for macOS that functions as an AI image generator and editor. It allows for the local generation of images from text prompts and the management of diffusion models without requiring external cloud services or technical setup. The application includes a local diffusion model manager for importing and switching between custom trained model files to achieve specific artistic styles. It also features a system for tracking generation history and uploading assets to a public gallery. The software covers several image synthesis and manipulation work
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