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This was the main question and it took me a second to find my footing.
Start by clarifying the role of a seller agent in Commerce Ads, then define output metrics across the funnel: from seller actions (e.g., ad creation, bid adjustments) to intermediate outcomes (e.g., impressions, clicks) and final business results (e.g., GMV, ROAS). Emphasize a multi-metric approach that balances short-term and long-term objectives, and discuss how to attribute results to the agent using causal inference or A/B testing.
Pro tip: Highlight the importance of aligning seller agent metrics with platform-level goals and avoiding vanity metrics; show you understand the trade-offs between optimizing for seller ROI and platform revenue.
Define what the seller agent does: e.g., automates ad campaign creation, bidding, budget allocation, or creative optimization. This sets the context for what outputs to measure.
Break down the seller's journey into stages: adoption (agent usage), intermediate performance (CTR, CVR, CPC), and final outcomes (ROAS, GMV, profit). List specific metrics for each stage.
Explain how to attribute results to the agent, e.g., A/B testing, holdout groups, or causal inference. Discuss challenges like selection bias and seasonality.
Discuss trade-offs: e.g., short-term ROAS vs. long-term seller retention, or platform revenue vs. seller profitability. Propose a composite metric or a dashboard with guardrail metrics.
Emphasize continuous monitoring, feedback loops, and adjusting metrics as the agent evolves. Mention the importance of offline evaluation and online experiments.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Follow-up question and honestly a pretty sharp one.
Start by explaining that exact string matching is insufficient for similar queries, so you need semantic understanding. Then describe a multi-layered approach: normalize and canonicalize queries, use embeddings with a similarity threshold, and incorporate a verification step to ensure the cached response matches the user's intent. Finally, discuss trade-offs between precision and recall, and how to handle edge cases like negation or entity swaps.
Pro tip: Mention that you would log mismatches and use them to continuously improve the similarity model, turning a potential failure into a feedback loop for better cache accuracy.
Apply text normalization (lowercasing, stemming, removing stop words) and canonicalize entities (e.g., dates, numbers) to reduce superficial differences. This ensures that queries differing only in formatting or synonyms map to the same cache key.
Encode queries into dense vectors using a model like BERT or Sentence-BERT, and compute cosine similarity. Set a high threshold (e.g., 0.95) to only serve cached results when queries are semantically near-identical, avoiding mismatches.
For queries above the threshold but not exact matches, run a lightweight verification step: check for critical differences (e.g., negation, entity swaps) using rules or a small classifier. If uncertain, bypass the cache and compute fresh.
If no sufficiently similar cached query exists, compute the answer and store it with its embedding. Log all cache hits/misses and mismatches to monitor performance and retrain the similarity model periodically.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.