Expertise

AI Evaluation and Content Intelligence

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

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

Full description

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.

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.

Current focus

N/A

Internal Links

Keep exploring

Explore with Alex OS

Evaluation is built into this CMS pipeline from the start — every LLM call is traced and every output is scored before it's trusted.

AI Evaluation & Content Intelligence

Generation alone isn't enough. Alex works on systems where AI outputs need to pass grounding, relevance, and consistency checks before they're acted on. The core problem: AI-generated content can be fluent but wrong, ungrounded, or inconsistent — and without a scoring layer, those outputs get published or applied automatically with no visibility into why.

How the evaluation pipeline works

The key tradeoff: automation speed vs. output trust

Automatic application of AI changes is faster but unsafe without a trust signal. Scoring and grounding checks slow the pipeline slightly but make the outputs auditable and defensible. Alex's approach accepts that tradeoff explicitly — the pipeline is designed to demote weak outputs rather than suppress them silently or pass them through unchecked.

Verifiable: live scoring and tracing in this CMS

The capability statement is specific: per-call LLM tracing (tokens, model, prompt, answer, cost) and content scoring before publish are both running in production in this CMS. Related experiments on this site test the same ideas at smaller scale — a RAG answer grounding test comparing grounded vs. ungrounded answers, and an AI content scoring experiment that scores quality automatically to prioritize review. The AI content review workflow shows the full loop: LLM review, scoring, and a publish gate.