RAG Makes Enterprise Generative AI a Reality

RAG works by attaching context found through retrieval to LLM generation, producing answers grounded in an organization's own knowledge. It compensates with organizational documents for the limitation that general models can explain things but struggle to reflect company-specific policies and customer context. As in the bank consultation example, it enables responses that incorporate policies, customer information, and internal materials together.

Use cases broadly split into internal and external.

• Internal: employee productivity tools that find knowledge across scattered documents and databases, analysis assistants that summarize financial, research, and market materials, training tools that help onboard new hires
• External: customer support chatbots that answer based on user information and help documents, public Q&A systems that give only document-grounded answers

The core of implementation is retrieval and augmented generation. Semantic search interprets query intent to find relevant documents, and vectors created via embedding are stored in a vector database to calculate similarity. Large documents are broken into smaller pieces via chunking to improve retrieval accuracy, after which Jurassic or Contextual Answers generates an answer based on that context.

The challenge lies in production. Everything must be handled: chunking and embedding optimization, processing of PDFs, DOCX files, and complex tables, ingestion pipelines connecting multiple storage systems, synchronization between source documents and indexes, reflecting user and group permissions, and security/regulatory requirements like GDPR and VPC. When data is scattered across Google Drive, OneDrive, S3, Google Storage, Confluence, Notion, Intercom, and Zendesk, the complexity grows even further.

AI21 RAG Engine is a managed package designed to reduce this burden.

• It bundles document extraction, chunking, embeddings, and vector storage and retrieval into a single API.
• It supports integration with sources like Google Drive and Amazon S3, along with automatic synchronization.
• It handles complex tables in PDF and DOCX files, along with index updates and permission management.
• As individual models, it separately offers Semantic Search, Text Segmentation, Embeddings, and Document Extraction.

Ultimately, enterprises can move RAG from experimentation to production, but building it in-house remains a task with significant time and maintenance costs.