Understanding What Matters for LLM Ingestion and Preprocessing

This article outlines crucial steps for preparing unstructured data for LLMs, emphasizing the need for effective ingestion and preprocessing. It discusses the transformation, cleaning, chunking, summarizing, and embedding generation processes for RAG-ready data, tailored specifically for enterprise-grade applications.

• RAG architectures involve a retriever module for querying prompt-relevant data.
• Data must be transformed into structured formats like JSON.
• Unstructured supports a variety of embedding models.
• Workflow orchestration is vital for LLM applications at scale.
• Unstructured provides state-of-the-art table extraction and smart-chunking.