LLM Evaluation and Observability Questions
Measuring and monitoring the quality of generative and LLM-powered systems. Covers evaluation approaches for open-ended outputs (human, model-graded, and reference-based), hallucination and safety checks, offline benchmarks versus online monitoring, and tracing and observability for production LLM applications. Emphasizes making non-deterministic systems measurable and trustworthy.
A production model update rolled out yesterday and today you are seeing degraded user satisfaction, but infra metrics (latency, CPU) look normal. As TPM, describe the step-by-step RCA process you'd lead to isolate whether the regression is due to the model change, dataset shift, prompt template change, or a downstream integration bug. Specify telemetry and experiments needed.
A deployed assistant generated content that led to a legal claim of defamation for a customer. As the TPM owning observability and evaluation, outline the immediate incident response (legal, communications, containment), short-term forensic steps, evidence to preserve for regulators, and long-term product and policy changes you would propose to reduce future legal risk.
You observe an unexpected spike in token usage and cost for a public endpoint. As TPM, outline a thorough investigation plan: the telemetry and logs to inspect, stakeholders to involve, hypotheses to test (for example prompt drift, bot abuse, model output length changes), and short/medium term mitigations to reduce cost while preserving customer SLAs.
You must build a business case to convince executives to invest in a unified LLM observability platform. As TPM, outline the key value levers (incident reduction, cost savings, faster feature launches, customer retention), propose metrics to quantify ROI, and present a prioritized MVP feature list that delivers measurable impact within 3 months.
Define 'input provenance' in the context of LLM observability. As a TPM, propose the required fields for an input-provenance telemetry schema (for example: request_id, api_key_hash, prompt_version, source, timestamp, user_context) and explain how each field supports debugging, auditability, and product analytics.
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