Register To Watch the On-Demand Webinar:
10x Your AI Data Analysis: The Context Engineering Formula That Works
Everyone wants AI to answer business questions with their data. Most AI analytics deployments disappoint, not because the models are bad, but because the data underneath them is untrustworthy, confusing, or stripped of business meaning. The model sees cryptic column names and has no idea what your data actually means.
Context engineering is the discipline that fixes this. In this on-demand webinar, DataKitchen practitioners walked through exactly how they build the three-layer foundation that turns a generic AI assistant into a domain expert: DT (Data Trust), DX (Data Experience), and CTX (Context). This is not a theory. They show the actual work: how we test and validate data so AI can trust it, how we curate semantic layers so AI can navigate it, and how we build and maintain context pipelines so AI can reason over it correctly.
You will see real examples from Snowflake and Databricks deployments, including how we transformed a 120-table pharma sandbox into a clean, AI-navigable schema, how we write business definitions that AI can actually use, and how context pipelines keep everything up to date as schemas and business rules evolve.
The result is analysts who spend less time validating AI outputs and more time generating insights. The formula is DT + DX + CTX = 10x. What You Will Learn
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How practitioners build Data Trust: automated testing, freshness monitoring, and quality gates that AI can rely on
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How practitioners build Data Experience: curating schema layers so AI stops guessing and starts reasoning
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How practitioners build Context: business definitions, example queries, ontologies, and domain knowledge that turn AI into a subject matter expert
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How context pipelines keep the AI layer current as your data and business rules change. Real before-and-after examples from Snowflake and Databricks deployments
Register now. Stop guessing why your AI keeps getting it wrong.
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