Best of Data Retrieval — July 2024

1. 1
   Article

   Chip Huyen·2y

   Building A Generative AI Platform

   The post details the construction of a generative AI platform, highlighting the common components such as context enhancement, guardrails, model routers, gateways, and caching techniques. It explores the complexities of context construction through retrieval-augmented generation (RAG) and the necessity of guardrails for input and output protection. The article also discusses the importance of adding routers and gateways for handling multiple models, optimizing for cost and latency, and ensuring security. Observability and orchestration principles for managing extensive AI application flows are also covered.

   129

   3
2. 2
   Article

   GoPenAI·2y

   Build Your Own CRAG System with Cohere: A Step-by-Step Guide to Improving Language Model Accuracy with Corrective Retrieval-Augmented Generation

   CRAG (Corrective Retrieval-Augmented Generation) aims to enhance the accuracy of language models. By using a lightweight retrieval evaluator, CRAG assesses the quality of retrieved documents, refining or discarding them based on confidence scores. It integrates with existing RAG systems, significantly improving their accuracy and generalizability across various tasks. CRAG remains stable even when retrieval quality declines, making it a promising addition to retrieval-augmented generation. The post provides a detailed guide on setting up a CRAG system using the LangGraph library and Cohere models.

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