Taming LLMs with Guided Generation Large Language Models (LLMs) are powerful but unpredictable. Getting them to output structured data can be challenging. While fine-tuning is resource-intensive, guided generation offers a middle ground. This technique uses constraints to steer the LLM's output without retraining. This article explores Microsoft's Guidance library and demonstrates its applications in: - Text Classification: Categorizing text into predefined groups (e.g., positive, negative, neutral). - Advanced Prompting: Implementing techniques like Chain-of-Thought (CoT) for enhanced reasoning. - Entity Extraction: Extracting specific information (dates, addresses) in a structured format. - Tool Use: Integrating LLMs with external tools for tasks like date calculation or string manipulation. Benefits - Enforces desired output format, eliminating post-processing. - Improves accuracy and predictability. - Can be faster than unconstrained generation. Drawbacks - Potentially slower in some cases. - May increase hallucinations by forcing unnatural output. Conclusion Guided generation, especially with tools like Guidance, offers a powerful way to enhance LLM usability. It improves predictability, simplifies integration with other tools, and reduces post-processing efforts. For code and a live demo, visit: Code: https://github.com/CVxTz/constrained_llm_generation Demo: https://guidance-app-kpbc8.ondigitalocean.app/