I Built a RAG Pipeline for F1 Team Radio, Then Made It Grade Itself

A Formula 1 team-radio retrieval-augmented generation project was developed to produce scoped summaries of drivers’ race-weekend comments, using the domain’s dense, timestamped, jargon-heavy communications to expose common RAG failures such as retrieving information from the wrong driver or session. The system stores synthetic radio messages in ChromaDB with session, driver, and lap metadata, retrieves relevant messages, and generates answers while recording nested traces through Opik, an open-source observability and evaluation platform. Its workflow progresses from ingestion and querying to evaluation with rule-based test suites and LLM-as-a-judge metrics for context recall and hallucination, followed by automated prompt optimization using MetaPrompt and versioned promotion of successful prompts to a prompt library. The author argues that observability, explicit evaluation criteria, optimization against measured results, and prompt version control are more important for reliability than producing a plausible initial demo. The approach has limitations, including variability in LLM judges, possible overfitting to evaluation data, and the likelihood that synthetic transcripts make retrieval appear more accurate than it would with noisy real-world audio transcriptions.

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