As teams move from RAG hype to real-world implementations, they quickly find one of the hardest parts of scaling RAG isn’t the language model. It’s managing the unstructured data pipeline.
Transforming raw documents into embeddings involves a complex sequence of steps: ingestion, preprocessing, parsing, enrichment, chunking, embedding, and indexing. Many teams build these pipelines using custom Python scripts, data loaders, and standalone tools. The result is often slow performance, limited scalability, increased costs and unreliable responses.
In this session, we’ll break down where these pipelines typically fail, walk through best practices (and a demo!) for the end-to-end flow from document to embedding, and outline what it takes to scale that process reliably in production.
What You’ll Learn:
✅ RAG implementation patterns and common challenges in data preparation vs. model serving
✅ Practical and highly-scalable approach to optimize every stage of the RAG pipeline with Ray and Anyscale
✅ Prompt template best practices to set up basic guardrails and accurate citations
✅ Product updates that make LLM offline (batch) and online (real-time) inference more scalable and performant
Who’s This For?
If you're an AI builder, architect, or decision maker exploring RAG, this session is for you. Whether you’re just getting started or scaling up, we’ll show you what it takes to ship reliable, enterprise grade systems and how Ray and Anyscale can help.