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Endigest AI Core Summary
Mazda built a GenAI-powered service assistant on Databricks Lakehouse to help hotline agents diagnose vehicle issues faster and more accurately.
•RAG architecture connects an LLM to a corpus of service information documents, warranty data, diagnostic codes, and vehicle history stored in Unity Catalog
•VIN is entered at session start to pre-load full vehicle context into the system prompt, eliminating tool call latency; the same toolbox is used when context is absent
•Unity Catalog user-defined functions filter the vector search index to only documents applicable to the specific vehicle before semantic matching runs
•MLflow 3's GenAI evaluation framework shifted testing from informal feedback to test-driven development using YAML evaluation datasets and LLM scorers
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Two data scientists delivered a working pilot in ~8 weeks; multilingual support was added by having the LLM translate prompts and responses without changing core architecture
This summary was automatically generated by AI based on the original article and may not be fully accurate.
Databricks
Google Cloud
Stripe