Most production RAG failures are not retrieval failures — they are data-readiness failures. This talk presents a pre-LLM data-readiness methodology built around labeling, embedding curation, similarity ranking, and noise reduction, with measurable impact on downstream answer quality.
We walk through the methodology, the failure modes it prevents, and how it integrates with hybrid retrieval over vector stores and knowledge graphs.
Takeaways: A reference methodology for pre-LLM data curation. A failure-mode catalog for production RAG. Integration patterns with existing retrieval stacks.

Preferred length: 30 min.
Audience: AI engineers, data engineers, RAG practitioners.
Level: Intermediate.
First public delivery: 2026.
Format: Conference talk, workshop section, podcast.