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Abhiram Ravikumar

Abhiram Ravikumar

@abhiramr

Stop Guessing: A Practical Playbook for Measuring Prompt Quality

Submitted Nov 12, 2025

Type of submission: 30 mins talk

Theme fit: Semantic layers & AI in practice

Overview

Teams often ship prompts that score well on BLEU or F1 yet fail in production with hallucinations, drift, and support escalations. This talk replaces intuition with an Evaluation Contract—clear, task-aligned metrics plus calibration, robustness, and cost/latency gates—so prompt changes ship with the same rigor as code. We’ll show where classic metrics help, where they mislead, and how to separate prompt quality from model variance using reproducible evaluation loops.

Demo: A pipeline that tests two FAQ prompts (A vs. B) on every pull request. Promptfoo runs the test matrix (including adversarial cases); DeepEval/Ragas score faithfulness and task success; Great Expectations validates data; Evidently tracks drift; and GitHub Actions blocks the merge if any check fails. In production, the same monitors continue watching—if hallucinations or drift spike, the system auto-rolls back to the safer prompt, keeping the pipeline reliable.

Why This Matters

As language model applications scale and diversify, intuition-driven or ad-hoc prompt evaluation leads to inconsistent, unreliable outcomes. Formal metrics allow practitioners to:

  • Compare prompts and make principled choices
  • Collaborate with shared standards across teams and projects

Without these tools, it’s impossible to ship robust, scalable, or safe prompt-powered systems.

Who Is the Talk For

  • AI/ML practitioners: AI engineers, data scientists, and LLM/prompt engineers taking prototypes to production.
  • Product & analytics leaders: AI/analytics PMs and BI leads who need trustworthy and repeatable evaluation benchmarks.
  • Safety researchers: Teams focused on measurement, robustness, calibration, and governance for LLM systems.

Key Takeaways

  1. A measurable playbook to evaluate, compare, and continuously monitor prompt quality—beyond gut feel.
  2. Practical tools and frameworks for prompt benchmarking and CI/CD: versioning, A/B tests, drift detection, and automated gates with examples using OSS tools and a spotlight on PromptLayer.
  3. Real-world, reproducible setups for prompt-evaluation experiments that you can clone and run directly.

Talk Structure (30 minutes total)

  1. Introduction & Problem Statement — 3 min
    Why intuition-driven evaluation fails at scale; the case for measurable metrics.
  2. Metrics That Matter — 5 min
    Accuracy, robustness, calibration, efficiency—what each reveals and where they mislead.
  3. Building the Evaluation Loop — 5 min
    Design reproducible A/B tests; separate prompt quality from model variance.
  4. Live Demo: Automated Evaluation & Production Practices — 8 min
  5. Challenges & Road Ahead — 4 min
    Interpretability, fairness, and where evaluation is heading.
  6. Q&A — 5 min

Additional Notes:

  • Video attached: PyCon 2018 talk on Rust (proof of prior speaking).
  • An earlier version of this talk was accepted at Prompt Engineering Conference 2025 (London); I couldn’t present due to scheduling/logistics.

Speaker Bio

Abhiram is a Senior Data Scientist at Publicis Sapient focused on NLP and LLM applications. A Mozilla Tech Speaker and regular presenter at Conf42, PyCon, and Mozilla Festival, he has shipped production NLP systems for CPG innovation and customer loyalty at Ai Palette and Collinson. He has published with IEEE and ACM, holds a Master’s in Data Science from King’s College London, and authored “Ultimate Transformer Models Using PyTorch 2.0”.

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