Evaluation & Benchmarks

An AI pipeline isn’t finished when it runs; it’s finished when it’s measured. I design evaluation harnesses that quantify how well retrieval, classification, and response generation are performing. These benchmarks ensure improvements are visible, reproducible, and defensible.

My Approach

  • Retrieval Metrics – Recall@k, nDCG@k, and MRR using real-world query sets derived from tickets, docs, and logs.

  • Intent Classification – Accuracy, F1, and abstain rate, with a hard ceiling on false positives.

  • Latency & Throughput – End-to-end p50/p95 response times, plus load tests to ensure the system holds up under stress.

  • Reproducibility – Evaluation scripts versioned alongside the pipeline, outputting results into dashboards for clear visibility.

Advancing Further

I continue to expand methodology toward:

  • Continuous Evaluation – CI/CD hooks that rerun benchmarks automatically with every new dataset or model release.

  • User-Centric Metrics – tracking “perceived accuracy” from user interactions alongside raw metrics.

  • Adaptive Benchmarks – evolving test sets that reflect changing domains and vocabularies.

Why It Matters

Models and pipelines drift, but benchmarks keep you honest. By measuring retrieval quality, classification accuracy, and system performance, I make sure that improvements are proven, not assumed.An AI pipeline isn’t finished when it runs; it’s finished when it’s measured. I design evaluation harnesses that quantify how well retrieval, classification, and response generation are performing. These benchmarks ensure improvements are visible, reproducible, and defensible.

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Semantic Search & Vector DB