Databricks Context Engineer Associate case studies
Databricks Context Engineer Associate · Put your knowledge to work in context.
The Databricks Certified Associate Context Engineer credential is aimed at practitioners who build the data and retrieval layers that feed generative AI applications on the Databricks Data Intelligence Platform. Rather than focusing on model training, it targets the work of turning raw, messy enterprise data into reliable context: ingesting from files, tables and streaming sources, cleaning and chunking it, storing it in governed Delta tables, embedding it, indexing it in Vector Search, and serving it to an application through retrieval-augmented generation. The Databricks Context Engineer Associate case studies are 5 cases and about 4,633 words.
1The 48,000-Token Policy Assistant That Got Worse as Recall Improved
A wealth-advisory firm runs an internal Policy Assistant on Databricks. Each request retrieves the top 40 chunks from a Delta Sync Vector Search index (roughly 48,000 tokens) and passes them, plus a 6,000-token system prompt, to a long-context chat model.
869 words, about 4 min2Nine-Hour Nightly Ingestion for Six Years of Clinical PDFs
A regional health system ingests clinical-guideline documents into a RAG corpus on Databricks. The source is ADLS Gen2 with 1.4 million PDFs and 90,000 DOCX files accumulated over six years, plus a JSON sidecar per file whose schema has changed three times: the field doc type became document type in v2 and doc class in v3.
959 words, about 5 min3Migrating 38 Million Chunks to a New Embedding Model Without Downtime
A SaaS company serves an in-app help assistant from a Databricks Vector Search Delta Sync index: 38 million chunks, 1024-dimensional embeddings from a general-purpose English model, hybrid search enabled, and a tenant id filter on every query. Peak load is 60 QPS with a p95 SLO of 400 ms end to end.
944 words, about 5 min4The Row Filter That Did Not Follow the Chunks
A global bank has 12 Databricks workspaces and one Unity Catalog metastore. A RAG assistant answers questions over HR policy, legal memos, and deal documents.
981 words, about 5 min5Offline Groundedness 0.91, Production Thumbs-Down 19%
An insurer's claims-status agent answers 40,000 questions a day. The weekly offline evaluation, using an LLM judge, reports groundedness 0.91 and correctness 0.87 against a 500-question golden set built eight months ago from contact-center transcripts.
880 words, about 4 min