Explains how to implement document retrieval with the Azure OpenAI Assistants API using a custom RAG approach, as the retrieval tool is not yet natively supported.
A simple explanation of Retrieval-Augmented Generation (RAG), covering its core components: LLMs, context, and vector databases.
A hands-on guide to evaluating LLMs and RAG systems using Langchain and Hugging Face, covering criteria-based and pairwise evaluation methods.
A guide to selecting the right LLM architectural patterns (like RAG, fine-tuning, caching) to solve common production challenges such as performance metrics and data constraints.
A practical guide outlining seven key patterns for integrating Large Language Models (LLMs) into robust, production-ready systems and products.
