Mastering RAG and Vector Search with Elastic
summary
Elastic lays out five steps for a RAG pipeline: ingest data, create embeddings (Elser, E5, hosted OpenAI or Hugging Face), chunk documents, vector-query with nearest-neighbor and optional BM25 hybrid reranking, then generate via an LLM and monitor. Elastic stores embeddings and supports document-level security, filtering, geo search and deployment on Cloud or on-prem. Production details include LangChain-compatible chaining, a user test that found “hello” as the most common prompt, and an ESQL alert rule for prompt-injection >2 times/day.
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chapters (6)
- 0:00RAG and Elastic overview
- 1:47Why RAG for private data
- 3:55Elastic AI assistants use cases
- 6:44Five steps to build a GenAI application
- 12:07GenAI app observability & security
- 13:31Implementation resources
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