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Open to talks and workshops

Expertise

AI Evaluation and Content Intelligence

Evaluating generated content, retrieval quality, and AI-assisted decisions.

Capability statement

This CMS traces every LLM call (tokens, model, prompt, answer, cost) and scores AI-generated content before it's trusted — evaluation is built into the pipeline, not bolted on after.

Problems I solve

  • Low-quality AI-generated content
  • Poor retrieval relevance
  • Unsafe automatic application of AI changes
  • Lack of visibility into why an output was selected

Approach

  • Score generated content before publish
  • Trace every LLM call for cost and behavior auditability
  • Require grounding for evidence claims
  • Demote ungrounded claims rather than presenting them as fact
I work with AI systems where generation alone isn't enough — outputs need to be checked for grounding, relevance, structure and consistency. I explore evaluation pipelines using scoring, validation and human review.
Evidence standard

Strengths

  • A live content-scoring pipeline and per-call LLM cost/behavior tracing already running in this CMS

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