Summary This repository provides a comprehensive, step-by-step guide for prompt engineering, specifically tailored for Upstage's Solar Large Language Models (LLMs). Aimed at non-expert users, it functions as a structured "cookbook" of practical techniques to optimize human-AI interactions and create highly effective prompts for diverse applications.

How It Works The project offers a collection of structured "recipes" detailing best practices for prompt creation with Solar models. It guides users from basic prompt structure to advanced techniques like few-shot, role/style, chain-of-thought, and hallucination resolution. Each chapter includes lessons, comparative prompt examples (good vs. bad), and practice sections, providing a framework for optimizing interactions and addressing common challenges.

Quick Start & Requirements The README mentions "Getting Started-Tutorial How-To" and environment setup chapters but provides no specific installation commands, non-default prerequisites (e.g., GPU, CUDA), or setup time estimates. The focus is on interacting with Upstage's Solar Pro model.

Highlighted Details

• Comprehensive guide for prompt engineering with Solar LLMs.
• Covers basic to advanced techniques, including chain-of-thought and hallucination resolution.
• Provides practical examples comparing good and bad prompt outputs.
• Offers guidance on when prompt engineering is preferable to fine-tuning (flexibility, cost, general tasks) versus when fine-tuning is needed (specialized tasks, consistency, scalability).

Maintenance & Community No specific details regarding contributors, community channels, sponsorships, or roadmap are provided in the README content.

Licensing & Compatibility The README content does not specify the project's license type or compatibility notes for commercial use.

Limitations & Caveats This cookbook is specifically designed for Upstage's Solar Pro model. Prompt engineering is presented as a flexible, cost-efficient alternative to fine-tuning for general tasks, but fine-tuning is recommended for highly specialized, domain-specific applications requiring critical consistency, accuracy, and scalability, or when the base model lacks essential knowledge.