This article demonstrates building a production-ready generative AI application for enterprise search using Retrieval Augmented Generation (RAG) with Haystack, SageMaker JumpStart, and OpenSearch.

• RAG technique retrieves relevant context from enterprise knowledge base before querying LLM
• Uses Falcon-40b-instruct model deployed via SageMaker JumpStart for response generation
• Haystack indexing pipeline preprocesses and indexes documents to OpenSearch vector database
• Embedding retriever filters top-k relevant documents using semantic similarity search
• Retrieved documents embedded into prompt to prevent LLM hallucinations and ensure accuracy
• Solution includes CloudFormation templates and Python scripts for easy deployment
• Customizable components: data sources, LLM models, prompts, embedding models, retrieval parameters
• OpenSearch and SageMaker provide security, access control, encryption, and auto-scaling capabilities

The post provides a complete implementation guide for building trustworthy enterprise search applications that ground LLM responses in company data.