[WEBINAR] RAG with OpenSearch: A Crash Course for Building RAG Applications

**Register here: https://www.linkedin.com/events/7350900293720584194/comments/**

In this workshop, you’ll learn how to build Retrieval-Augmented Generation (RAG) applications with OpenSearch from end to end.

We’ll cover the essentials: how to preprocess your data, generate and use embeddings, and store them in a vector database.

You’ll see how to combine traditional and semantic search, and how to use pre- and post-processing to improve results. By the end, you’ll know how to set up a simple but effective RAG pipeline with OpenSearch - covering everything from data ingestion to generating answers with large language models.

We'll walk through the full RAG pipeline, including:

- **Data preparation**: Structuring documents for chunking, metadata enrichment, and indexing
- **Embeddings**: Choosing the right model and generating semantic vectors at scale
- **Vector storage**: Storing and retrieving vectors efficiently in OpenSearch with k-NN or HNSW
- **Hybrid search**: Blending keyword-based and semantic retrieval for better relevance
- **LLM integration**: Passing context to your language model and shaping better responses with pre- and post-processing techniques

You'll leave with a clear understanding of how to set up and optimize a RAG system with OpenSearch - from ingestion and indexing to querying and generating accurate, grounded answers with LLMs.

**Ideal for** developers, data engineers, and AI practitioners looking to move from proof-of-concept to production-ready RAG solutions.