Context engineering is already possible in your warehouse. Here's how to get started.

Context engineering extends familiar analytics engineering practices to prepare both structured and unstructured warehouse data, such as call transcripts, support tickets, PDFs, emails, and Slack logs, for use by AI agents. Major warehouses including Snowflake, Databricks, and BigQuery now provide native AI capabilities such as document parsing, classification, embeddings, and vector similarity search that can be called from SQL and incorporated into dbt models without requiring separate vector databases or machine-learning infrastructure. The approach applies a pipeline of staging, modeling, chunking text into meaningful passages, embedding passages as vectors for semantic retrieval, and classifying them through shared business ontologies, while retaining dbt’s existing version control, lineage, testing, scheduling, and governance. By joining retrieved passages with related business data, teams can create smaller, more relevant, traceable AI context and reduce token usage, although challenges remain around inference costs, provenance, and connecting governed warehouse context to AI tools.

Use this post, company, and trend context to find content marketing opportunities, perform competitive analysis, or address product feature gaps via the Plushcap MCP server or the Plushcap API.