SnowPro Specialty: Gen AI Question 287
Single answerTrulens SDKA data science team has deployed a retrieval-augmented generation (RAG) chatbot in Snowflake and wants to evaluate it before wider rollout. They need to capture each app interaction, compare runs over time, and score outputs on dimensions such as groundedness and answer relevance using the TruLens SDK. Which approach best meets these requirements?
- A
Instrument the RAG application with a TruLens recorder, define feedback functions for metrics such as groundedness and answer relevance, and review the recorded evaluations in the TruLens dashboard.
- B
Use TruLens only after exporting chatbot logs to a CSV file, because the SDK cannot evaluate live application calls directly.
- C
Replace the retriever with a larger embedding model, because TruLens focuses on vector quality and does not support response-level evaluation metrics.
- D
Run SQL queries over Cortex Analyst metadata tables, because TruLens metrics are automatically stored there without any application instrumentation.
Show answer and explanation
Correct answer: A
Explanation
The best answer is to instrument the application with TruLens, record executions, and attach feedback functions to measure dimensions important for RAG quality, such as groundedness and answer relevance. In practice, TruLens is used to observe and evaluate LLM app behavior, not merely to inspect static logs after the fact. This aligns with common best practices for GenAI evaluation: capture real application traces, define explicit quality metrics, and compare results over time to guide prompt, retrieval, and orchestration improvements. Snowflake documentation and TruLens usage patterns emphasize evaluation through app recording and feedback functions rather than assuming automatic storage in Snowflake system metadata or limiting evaluation to offline CSV analysis.
- A. Correct.
Correct. TruLens is designed to instrument LLM applications, including RAG pipelines, so developers can record app executions and attach feedback functions that evaluate qualities such as groundedness, relevance, and related response quality signals. This supports iterative evaluation and comparison of runs over time through the TruLens interface and stored records.
- B. Incorrect.
Incorrect. This reflects a common misconception that evaluation must be done only as a separate offline step. TruLens supports instrumentation of application calls so evaluations can be tied directly to actual executions, rather than requiring a CSV-export-only workflow.
- C. Incorrect.
Incorrect. Improving embeddings may help retrieval quality, but it does not address the requirement to systematically record interactions and score outputs. TruLens is not limited to vector or embedding inspection; it is used for application-level evaluation, including feedback on generated responses in RAG systems.
- D. Incorrect.
Incorrect. TruLens does not automatically evaluate an application just because it runs in Snowflake, and application instrumentation is still needed to capture traces and apply feedback functions. This option also confuses TruLens with other Snowflake metadata or analytics capabilities.