Save Time and Effort When Building LLM Apps Using Guided Generation

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/