Contextual AI

Forget RAG Pipelines—Build Production Ready Agents in 15 Mins: Nina Lopatina, Rajiv Shah, Contextual

Jun 27, 2025 · 1:15:43

Rajiv Shah, Nina Lopatina, and Matthew from Contextual AI demonstrate how developers can build production-ready RAG agents in minutes using Contextual AI's managed RAG platform, emphasizing that RAG should be treated as a managed service to avoid reinventing infrastructure. They walk through ingesting documents like NVIDIA financials and spurious correlation reports, then querying the agent with questions requiring quantitative reasoning across tables. The platform handles extraction, layout analysis, image captioning, hybrid retrieval, and a state-of-the-art reranker, capped by a grounded language model that avoids hallucinations and provides attribution. Evaluation is done via LM Unit, a model-as-judge that scores responses on criteria like accuracy and causation. The episode also shows integrating the agent with Claude Desktop via MCP and answers audience questions on pricing (consumption-based with a $25 credit), scalability, entitlements, HIPAA, and domain-specific language.

RAG Agents in Prod: 10 Lessons We Learned — Douwe Kiela, creator of RAG

Apr 10, 2025 · 16:56

Douwe Kiela, CEO of Contextual AI and creator of RAG, shares 10 lessons from deploying enterprise RAG systems at scale. He argues that language models are only 20% of a larger system; success comes from focusing on systems, not models, and specializing over AGI to unlock domain expertise. Enterprise data is the real moat, but pilots are easy while production is hard—design for production from day one. Speed beats perfection: ship barely functional to real users early and iterate. Avoid boring engineering chores like chunking; instead, integrate AI into existing workflows to drive adoption. Accuracy is table stakes; handle inaccuracy with observability and attribution. Be ambitious: aim for transformative ROI, not low-hanging fruit like basic HR questions.