AI Knowledge Systems Portfolio

AI responses are only as reliable as the knowledge systems behind them.

I design and evaluate knowledge systems for AI-enabled teams: response quality, retrieval readiness, workflow design, and content operations.

Best aligned with AI content operations, knowledge systems strategy, AI evaluation, and AI product operations roles where knowledge quality affects AI outcomes.

Process Evaluate outputs Diagnose knowledge gaps Scope interventions Measure change
Case Study · AI Response Quality

Why most low-scoring AI responses were incomplete, not incorrect

Evaluation of 949 GenAI responses across seven product teams showed that low-scoring answers were usually incomplete rather than outright incorrect — a distinction that changed the diagnosis and the fix.

Failure-pattern analysis of 278 low-scoring responses

Incomplete 71% · Incorrect 13% · Correct, weak 11% · Uncategorized 5%

Produced: team scorecards, failure-pattern analysis, prioritized fixes, and review flags for writers, SMEs, and engineering.
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Case Study · Retrieval Readiness

Assessing retrieval readiness across five content workstreams

A cross-functional working group assessed documentation readiness for RAG-style workflows and produced shared guidance for AI-ready content design.

20–30 contributors · 5 workstreams coordinated

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Case Study · Measurement

Turning scattered workflow feedback into a prioritization baseline

UX measurement helped a platform team identify what to fix, what to validate, and where workflow changes were improving the authoring experience.

CCMS pilot · SUS score: 39 → 57 across cycles 1–3

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Case Studies Where it was applied

Documented project examples grouped by capability and outcome.

 Resources What can be reused

Kits, templates, and tools adapted from the documented work.

 Methods & Patterns How the work generalizes

Repeatable methods and recurring problems observed across evaluation, retrieval readiness, workflow scoping, and UX measurement work.

Resources

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Kit AI Answerability Audit Kit Diagnose why AI responses fail and whether the knowledge layer is part of the root cause. Includes an audit template, scoring guide, and interpretation notes.
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