How you would evaluate LLM Quality and Performance?
Product Management Question asked at Google, OpenAI, Anthropic: Walk me through how you’ve evaluated LLM quality and performance in a production environment.
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Google, OpenAI, Anthropic Product Management Interview Question:
How you would evaluate LLM Quality and Performance?
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Before looking at metrics or dashboards, I would first get clear on what “Good Quality” actually means for this product. I would do that by answering four simple questions:
What is the use case?
For example: a chat assistant, RAG system, AI agent, code generation, summarization, or classification.What is the user trying to achieve?
Is the goal accuracy, speed, creativity, trust, or automation such as reducing manual work?What happens if the model is wrong?
Is it a minor annoyance, or does it create business, legal, or safety risk? Higher risk means stricter guardrails.What stage is the product in?
Is this an early pilot, a limited rollout, or a fully scaled production system?
Answering these upfront helps ensure I evaluate the LLM against the right expectations, not a generic standard.
Three-layer Success Model
I would evaluate LLMs in this order,
Outcome quality
Did the user actually complete the task they came for? This reflects real business and user value.Model behavior quality
Are the responses correct, consistent, and aligned with instructions and safety expectations? This is about trust and reliability.System performance
Can the system respond quickly, handle scale, stay available, and operate within cost limits?
This order is intentional. It ensures we never trade correctness or user trust for faster responses or lower costs.
Map Goals to User Actions & Metrics
I start by translating the product goal into a simple user journey and then add measurement at every step. This makes it easy to see where users struggle and where the experience breaks down.
The typical user flow looks like this:
The user submits a prompt.
The model generates a response.
The user reviews the output and may edit or regenerate it.
The user either accepts the result or abandons the task.
The user returns later to do a similar task again.
By attaching the right metrics to each step, I would clearly identify where friction exists and which part of the experience needs improvement.
