LLM evaluation and AI benchmarking for production teams

Why LLM features need an evaluation suite

Most teams test an LLM feature by trying a handful of prompts and reading the answers. That works for a demo. It stops working the first time you switch model version, rewrite the system prompt or change how documents are retrieved, because nobody can say whether the change helped across the hundreds of cases that matter.

An evaluation suite turns that guesswork into a number you can track. It tells you whether a cheaper model is good enough, whether a prompt change fixed one problem and broke two others, and whether the feature is ready to ship. It also gives you something concrete to show enterprise buyers who ask how you know your AI is accurate.

• Choosing between models and providers on your tasks, not on public leaderboards
• Catching regressions before a prompt, model or retrieval change reaches users
• Deciding when a feature is ready to launch, with a pass/fail bar agreed in advance
• Answering customer and investor questions about accuracy with evidence

What I build: benchmarks, golden datasets and scoring rubrics

A useful evaluation starts from your users’ real tasks, not a generic benchmark. I work with your domain experts to collect representative cases, including the awkward ones, and agree what a good answer looks like before any model is scored.

Ground-truth data is written so it can’t be found by searching the web or copied from training data, which keeps scores honest. Where a task has more than one acceptable answer, the rubric scores the parts that matter, such as correctness, completeness, following the spec and handling edge cases, rather than matching exact wording.

• Golden datasets: reviewed input and expected-output pairs drawn from your real use cases
• Benchmarks for multi-step tasks, from coding and system design to document-heavy business workflows
• Scoring rubrics with clear pass/fail criteria that two reviewers would apply the same way
• Adversarial cases: conflicting documents, outdated data and confident but wrong instructions planted to test blind trust
• Metrics for cost, latency and token use alongside quality
• Regression tests that run in CI on every change to prompts, models, tools or retrieval

AI agent evaluation: trajectories, not just final answers

Agents need a different kind of evaluation, because the final answer hides most of what happened. An agent can reach the right result by luck, take ten steps where two would do, or call a tool it should never have touched. I evaluate the whole trajectory: what the agent was asked, what it planned, which tools it called with which arguments, and whether each step moved the task forward.

Long-horizon evaluation of autonomous software-engineering agents is a core part of my evaluation work, including trajectory reviews and multi-turn reasoning audits. Your agent gets the same scrutiny, scored step by step as well as on the outcome.

• Task success checked against the real system state, not the agent’s own summary
• Step efficiency, wasted tool calls, loops and token spend per task
• Tool choice and arguments, including actions outside what the user asked for
• Recovery from errors, missing data and tools that fail or time out

Catching reward hacking and false “done” claims

The most expensive evaluation failure is a test that passes when it shouldn’t. Agents learn to satisfy the check rather than do the work: editing a test so it passes, hard-coding an expected value, or reporting a task as complete when nothing changed. Evaluation suites can leak answers in the same way, through test files, fixtures or prompts that give the result away.

Much of my evaluation work is auditing exactly this: reviewing synthetic repositories, test suites and Docker runtimes for reward hacking, answer leakage and false-positive verification, and fixing brittle tests and mismatches between prompts and verifiers. I review your evaluation with the same assumption, that anything which can be gamed eventually will be.

It is also why Brakeproof, the safety layer for AI agents I founded, includes proof-of-work checks. When an agent says a payment was refunded, a pull request was merged or a record was updated, Brakeproof checks Stripe, GitHub or the database to confirm it. I build the same kind of independent check into the evaluation suites I design for clients.

LLM-as-judge, calibrated against human review

Human review doesn’t scale to every commit, so most teams end up using a model to grade another model’s output. That works only if the judge agrees with your experts. An uncalibrated judge can favour longer answers, miss subtle errors or reward confident wording, and the scores drift every time the judge model changes.

I calibrate the judge before anyone relies on it. A sample of outputs is scored by your experts and by the judge, the disagreements are studied, and the judge prompt and rubric are tightened until they match. Cases where the judge stays unreliable are flagged for human review rather than scored automatically.

• Judge prompts and rubrics written for each task type
• Agreement checks between the judge and human reviewers on a held-out sample
• Deterministic checks, such as running code or querying data, wherever a judge isn’t needed
• Calibration notes your team can repeat when the judge model changes

Frontier-model evaluation experience

Through Toptal, I work as an AI evaluation and benchmark engineer for a US-based frontier AI research lab. I design realistic multi-step coding, system-architecture and algorithmic-reasoning benchmarks from production-grade codebases, evaluate model-generated code and reasoning traces for correctness, efficiency, edge cases and spec alignment, and write original ground-truth datasets, rubrics and calibration feedback for automated evaluation suites.

Alongside that, I’ve done evaluation, red-teaming and training work on frontier LLMs and autonomous agents through Mercor, Turing, Uber AI Solutions, DataAnnotation, Mindrift, AfterQuery and Terac. It covers multi-domain benchmark frameworks, adversarial evaluation pipelines, enterprise simulation suites built from many documents, trajectory audits, and benchmarking pipelines for correctness, token efficiency and latency.

I’m a Databricks Certified Generative AI Engineer, with 11+ years in engineering, and I build production AI systems as well as evaluating them. If you also need your AI tested under attack, that is a separate service: AI red teaming covers prompt injection, jailbreaks and unsafe tool use.