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 private data sources for accurate, context-aware responses. Furthermore, it covers sophisticated techniques such as chain-of-thought prompting to improve logical reasoning, and model-driven entity extraction to transform unstructured text into structured knowledge graphs or database queries.
Beyond these core patterns, the repository offers a wide range of automated text analysis capabilities, including document summarization and natural language data classification. These recipes are designed to help engineers streamline data processing tasks and build robust, production-ready workflows.
Each guide is provided as a self-contained Jupyter Notebook, including the necessary code and data to execute the examples. Users can get started by navigating to a specific directory and following the instructions within the provided notebook files.
