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// the part most teams skipEvaluation & Quality Infrastructure

Evaluation & Quality Infrastructure

You shipped an AI feature. It seemed to work in testing. But now it's in production and you have no idea if it's still good — no metrics, no regression tests, no alerts when quality degrades. I build the infrastructure that turns "it seems fine" into "here's exactly how it's performing and here's what changed."

// the problemThe Problem

You can't improve what you can't measure

Most AI systems ship without evaluation infrastructure. The team does some manual spot-checking, decides it looks good, and moves on. Then three months later, someone notices the outputs seem worse — but nobody can say when it started, what changed, or how bad it actually is. There's no baseline to compare against, no automated tests to catch regressions, and no monitoring to flag drift.

This is how AI projects lose trust internally. Not from a dramatic failure, but from a slow, invisible degradation that nobody catches until a stakeholder asks "is this thing even working?" and the team can't answer confidently.

Evaluation infrastructure fixes this. It gives you numbers instead of vibes, alerts instead of surprises, and a systematic way to make your AI system better over time.

// what these look likeWhat These Look Like

The spectrum

Evaluation infrastructure ranges from a basic test suite that catches obvious regressions to a comprehensive quality platform with real-time monitoring, automated benchmarking, and cost optimization. Where you start depends on how mature your AI system is and how much is at stake.

◆ Eval harnesses & test suites foundational

A structured set of test cases that measure your AI system's performance on known inputs with expected outputs. Run before every deployment, on a schedule, or on demand. Catches regressions before they hit production. The minimum viable quality infrastructure — if you have nothing else, start here.

◆ Production monitoring & alerting operational

Real-time tracking of your AI system's behavior in production — output quality, latency, cost per request, error rates, confidence distributions. Automated alerts when metrics drift outside normal ranges. Dashboards that let your team see system health at a glance without digging through logs.

◆ Comprehensive quality platform advanced

Full evaluation lifecycle — automated benchmarking across model versions, A/B testing infrastructure, human feedback collection pipelines, cost optimization analysis, and systematic quality improvement workflows. For teams running AI at scale where quality directly impacts revenue or safety.

// examples of what I've builtExamples

This looks like

LLM output evaluation harness
Team shipping an LLM-powered feature with no way to measure output quality beyond manual review. Built an eval suite with curated test cases, automated scoring (both heuristic and LLM-as-judge), and a CI integration that blocks deployments when quality drops below threshold. The team went from "it looks fine" to "accuracy is 94.2% on our test set, down 1.3% from last week" in a matter of days.
RAG quality monitoring dashboard
RAG system in production with no visibility into retrieval quality. Built a monitoring layer that tracks retrieval relevance scores, answer groundedness, citation accuracy, and user feedback signals — displayed on a live dashboard with automated alerts when any metric drifts. Identified a chunking regression within hours of a document re-index that would have gone unnoticed for weeks.
Model performance tracking across versions
ML team retraining models regularly but evaluating each version with ad-hoc scripts and different test sets. Built a standardized benchmarking pipeline that evaluates every model version against the same holdout sets, tracks performance over time, compares champion vs. challenger models, and produces a summary report for each retraining cycle.
Cost and latency optimization audit
LLM-powered product with costs growing faster than usage. Instrumented the entire inference pipeline — token counts per request, model routing decisions, cache hit rates, latency percentiles — and identified that 40% of requests could be handled by a smaller model with no quality difference. Built the routing logic and monitoring to prove it.
Human feedback collection pipeline
AI system generating outputs that needed periodic human review, but feedback was going into a spreadsheet that nobody analyzed. Built a structured feedback collection interface, connected it to the eval pipeline so human judgments automatically update quality metrics, and added a weekly report that surfaces the most common failure modes — turning scattered feedback into actionable improvement data.
// under the hoodHow It Works

How these are built

Evaluation infrastructure isn't a product you install — it's custom-built around your specific AI system, your quality requirements, and the metrics that actually matter for your use case. Here's what goes into it.

Test case design
The hardest and most important part. I work with your team to build a curated test set that covers normal cases, edge cases, adversarial inputs, and the specific failure modes you care about. Good test cases are more valuable than sophisticated scoring — a well-designed suite of 200 examples will tell you more than a sloppy suite of 2,000.
Automated scoring
Different outputs need different evaluation strategies. Exact-match for structured extraction, semantic similarity for open-ended generation, LLM-as-judge for nuanced quality assessment, custom heuristics for domain-specific criteria. I build scoring pipelines that combine multiple strategies and produce a single, interpretable quality score.
Production instrumentation
Lightweight logging that captures inputs, outputs, latency, token usage, confidence scores, and model metadata for every request — without impacting production performance. Structured so you can query and analyze patterns across thousands of requests, not just inspect individual ones.
Dashboards & alerting
Monitoring that your team will actually look at. Quality metrics over time, cost breakdowns, latency percentiles, error rates, drift indicators — surfaced in dashboards that answer the questions your team actually asks. Alerts configured for the thresholds that matter, not noise that gets ignored.
CI/CD integration
Eval suites that run automatically — on every deployment, on a schedule, or triggered by data changes. Quality gates that block deployments when metrics drop. Version comparison reports that show exactly what changed between model or prompt versions. Evaluation as part of the development workflow, not an afterthought.
// how we work togetherWorking Together

The process

01
Scoping call
30 minutes, free. You walk me through your AI system — what it does, how it's deployed, what you currently know (and don't know) about its quality. I'll help identify the biggest gaps and where eval infrastructure would have the most impact.
02
Quality audit
I review your existing system — the model, the prompts, the data pipeline, any existing tests or monitoring. I run an initial evaluation to establish a baseline: here's how your system performs today, here's where it's strong, here's where it's failing. This is the document your team has been missing.
03
Build eval infrastructure
Test suite design, automated scoring, production instrumentation, dashboards, alerting — built in stages with your team reviewing and refining along the way. I make sure the infrastructure answers the questions your team actually needs answered, not generic metrics that look good but don't inform decisions.
04
Hand off & integrate
The eval infrastructure becomes part of your development workflow — CI integration, scheduled runs, dashboards your team checks regularly. I document everything and train your team on how to maintain and extend the test suite as your system evolves.
// pricingPricing

What this costs

It depends on the scope of your AI system, the complexity of your evaluation needs, and how much production monitoring infrastructure is involved. A focused eval harness for a single feature is a smaller engagement than a comprehensive quality platform across multiple AI systems.

The scoping call is free. I'll assess your current evaluation gaps and give you an honest recommendation on where to start — sometimes the highest-impact move is a simple test suite, not a full monitoring platform.

// common questionsCommon Questions

Frequently asked

Q: How do you evaluate LLM outputs when there's no single "right answer"?
This is the central challenge and there are several approaches that work well in combination. LLM-as-judge scoring with calibrated rubrics, semantic similarity against reference answers, heuristic checks for specific criteria (length, format, presence of citations), and structured human feedback collection. The key is defining what "good" means for your specific use case and building scoring that aligns with that definition.
Q: We already have some tests — is it worth investing more here?
Maybe. The question is whether your existing tests actually catch the failures you care about. I often see test suites that pass consistently but miss real production issues — because the test cases are too easy, the scoring is too lenient, or the tests don't cover the edge cases where the system actually breaks. A quick audit can tell you if your current tests are giving you real signal or false confidence.
Q: Can you add eval to an existing system without disrupting it?
Yes — that's the typical engagement. Evaluation infrastructure wraps around your existing system; it doesn't require rewriting it. Production instrumentation is designed to be lightweight (logging, not processing). Test suites run in parallel, not inline. The goal is visibility into what's already running, not changing how it runs.
Q: What metrics should we be tracking?
It depends entirely on your use case. For RAG systems: retrieval relevance, answer groundedness, citation accuracy. For LLM features: task completion rate, factual correctness, format compliance. For all AI systems: latency, cost per request, error rate, confidence distribution. I help you identify which metrics actually matter for your decisions and skip the vanity metrics that look good in a report but don't inform action.
Q: How much ongoing maintenance does eval infrastructure require?
Less than you'd think once it's set up properly. The automated pieces (monitoring, alerting, CI integration) run on their own. The main maintenance task is evolving the test suite as your system changes — adding new test cases for new features, updating expected outputs when behavior intentionally changes, and periodically reviewing which failure modes matter most. I design it so this feels like a natural part of development, not a separate chore.
Q: Should we build this ourselves?
You could — and eventually your team should own it. The value of having me build it is speed and experience. I've designed eval systems for multiple AI architectures and I know which patterns work, which scoring strategies are reliable, and where teams typically waste time. I build it, hand it off with documentation, and your team extends it from there.
// related servicesRelated Services

Related services

rag systems — every RAG system I build ships with eval infrastructure included
custom models — evaluation and monitoring built into the model lifecycle
strategy — includes evaluation gap analysis as part of architecture review
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