AI evaluation guides

The full evaluation loop for a shipped AI feature: how to build a test dataset, how to run structured error analysis, how to turn what you learn into rubrics, and how to layer LLM judges and deterministic checks on top so you catch regressions before your users do.

Article30 AUG 2026

How to build an AI evaluation rubric from real outputs

A practical four-step method for building an AI evaluation rubric from real product failures: run, annotate, find patterns, and turn those patterns into checks.

Catalina Turlea

The ultimate guide to building an LLM judge you can trust

Article05 AUG 2026

The ultimate guide to building an LLM judge you can trust

A step-by-step guide to building and validating an LLM-as-a-judge — grounded in a benchmark of 8 judges across 11 models and 3,000+ verdicts. Generic judge templates agreed with human experts only 52–58% of the time. Here's the process that gets you a judge you can trust.

Madalina Turlea

Braintrust vs Lovelaice: which AI evaluation platform fits your team in 2026

Article01 AUG 2026

Braintrust vs Lovelaice: which AI evaluation platform fits your team in 2026

A feature-by-feature review of Braintrust vs Lovelaice: multi-model experiments, blind evaluation, human review, custom metrics, LLM judge setup, and judge validation against human labels. Which platform is best for product managers, cross-functional teams, and building an LLM judge you can trust — and where Braintrust is genuinely stronger.

Madalina Turlea

How to move past vibe checks: scaling manual AI testing into systematic evaluation

Article16 JUN 2026

How to move past vibe checks: scaling manual AI testing into systematic evaluation

Vibe checks are the right way to start testing AI — and the wrong way to keep going. The step-by-step path from a few happy-path tests to systematic, automated evaluation.

Madalina Turlea

AI evals for product managers: the complete guide for 2026

Article16 JUN 2026

AI evals for product managers: the complete guide for 2026

The complete, practical guide to AI evals for product managers in 2026 — what an eval is, why it's a PM skill, and how to evaluate AI quality whether you have a live feature or just an idea.

Madalina Turlea

The alternative to Confident AI for product teams (2026)

Article11 JUN 2026

The alternative to Confident AI for product teams (2026)

Confident AI is a strong, PM-focused eval tool — real error analysis, real metric mapping. The gap is in the judge workflow: deploy first, find mismatches later. Here's the alternative that calibrates your judge against human judgment before you trust it in production.

Madalina Turlea

Best AI evaluation tools for product managers in 2026 (ranked by a PM)

Article10 JUN 2026

Best AI evaluation tools for product managers in 2026 (ranked by a PM)

A PM's ranking of the 8 best AI evaluation tools in 2026 — Lovelaice, Confident AI, LangSmith, Braintrust, Maxim AI, Adaline, Langfuse, and PostHog — researched from official docs and tested hands-on. Judged on what a PM can actually do without engineering: run experiments before shipping, review outputs blind with domain experts, and build validated metrics.

Madalina Turlea

The alternative to Langfuse for product teams (2026)

Article10 JUN 2026

The alternative to Langfuse for product teams (2026)

Langfuse is open-source LLM engineering infrastructure — built for developers first. If your team needs a proven evaluation process fast, with domain experts and PMs contributing to AI quality, here's the alternative: guided validation the whole team can run.

Madalina Turlea

The alternative to Braintrust for product teams (2026)

Article10 JUN 2026

The alternative to Braintrust for product teams (2026)

Braintrust is built for engineering-first teams. If you're looking for an alternative platform to guide your PMs and domain experts through pre-shipment AI validation themselves — no engineering ticket required — here's the case for Lovelaice.

Madalina Turlea

How to measure AI feature ROI (when half of teams can't)

Article10 JUN 2026

How to measure AI feature ROI (when half of teams can't)

Half of product teams name unclear ROI as their #1 AI challenge, and a third track no AI metrics at all. Here's a measurable framework for the return on a single AI feature.

Madalina Turlea

Why AI features fail: the silent failure problem

Article10 JUN 2026

Why AI features fail: the silent failure problem

Only 7.8% of teams say their shipped AI features delivered measurable impact. Why AI features fail silently, why teams don't notice, and how to catch it before users leave.

Madalina Turlea

How to know your AI feature actually works in production

Article26 MAY 2026

How to know your AI feature actually works in production

Most teams ship AI features and wait for complaints. Here's how product managers can know — not hope — their AI is working in production.

Catalina Turlea

How to validate an LLM judge before you trust it

Article19 MAY 2026

How to validate an LLM judge before you trust it

The annotate → encode → validate → iterate loop is the only thing that moved our judge's agreement with a human expert from 65% to 76.5%. The step-by-step workflow, with the traps we hit written in.

Madalina Turlea

What is AI experimentation, and why do you need it?

Article14 MAY 2026

What is AI experimentation, and why do you need it?

One idea. One prompt. Five real cases. Several models. Read every response. That's where AI-native products start. An 8-step playbook for product thinkers running their first experiment.

Madalina Turlea

Why your AI evaluation is lying to you

Newsletter31 MAR 2026

Why your AI evaluation is lying to you

Why most teams automate AI evaluation before understanding what they're evaluating — and the step-by-step approach that actually works.

Madalina Turlea

Evals in CI are only as smart as the engineer who wrote the assertion

Article12 MAR 2026

Evals in CI are only as smart as the engineer who wrote the assertion

Putting evals in CI is good engineering. It is also silent on every failure mode the person who wrote the test did not know to worry about — which in fintech, healthtech, and insurance is the ones that ship the bug.

Catalina Turlea

If the harness is the moat, evaluation is how you defend it

Article12 MAR 2026

If the harness is the moat, evaluation is how you defend it

If models are becoming interchangeable and the agent harness is the moat, evaluation is what turns the loop into an advantage you can prove — on your domain's definition of right, all the way through the deep runs where the interesting failures hide.

Catalina Turlea

Why 90% survey satisfaction and 40% churn can coexist

Article15 JAN 2026

Why 90% survey satisfaction and 40% churn can coexist

Your AI feature scores high on satisfaction surveys but users keep leaving. Here's why silent AI failures hide behind good survey numbers.

Madalina Turlea

Your AI always returns an answer. That's why you can't trust it.

Article15 JAN 2026

Your AI always returns an answer. That's why you can't trust it.

AI features never throw errors — they just quietly get things wrong. Here's how product managers catch silent failures before users lose trust.

Madalina Turlea

What thumbs up/down feedback actually tells you about your AI feature

Article15 JAN 2026

What thumbs up/down feedback actually tells you about your AI feature

Thumbs up/down feedback on AI features measures user sentiment, not output quality. Learn what binary feedback misses and what to measure instead.

Madalina Turlea

The silent failure loop: how you lose users without a single complaint

Article15 JAN 2026

The silent failure loop: how you lose users without a single complaint

AI features don't fail loudly. They fail silently — and users leave before you notice. Here's how product teams break the loop.

Madalina Turlea

What does "the AI is working" actually mean?

Article15 JAN 2026

What does "the AI is working" actually mean?

Most product teams can't define what 'working' means for their AI feature. Here's the definition that separates shipping with confidence from Ship and Pray.

Madalina Turlea

LLM-as-a-judge: how to evaluate AI features without checking every answer by hand

Article15 JAN 2026

LLM-as-a-judge: how to evaluate AI features without checking every answer by hand

Most teams build the wrong judge — vague rubrics, one-to-five scoring, no validation. The three-stage evaluation ladder, why a judge should be binary and single-error, and how one prompt went from 50% to 93% agreement with humans.

Madalina Turlea

Error analysis for AI: turning messy review notes into a fix list

Article15 JAN 2026

Error analysis for AI: turning messy review notes into a fix list

You cannot improve an AI feature until you know how it actually fails — not how you think it fails. The unglamorous front end of every reliable AI feature: read, annotate, group by root cause.

Madalina Turlea

How to run an AI experiment, step by step

Article15 JAN 2026

How to run an AI experiment, step by step

From idea to evaluation: how to write the first prompt, build realistic test cases, pick the right mix of models, review the answers side by side, and turn your notes into something you can scale.

Madalina Turlea

How to build a test dataset for an AI feature

Article15 JAN 2026

How to build a test dataset for an AI feature

A good test set is the difference between a feature that looks fine in a quick check and one you can actually trust. How to replicate real usage, design inputs that try to break the feature, and grow the set as you learn.

Madalina Turlea

Deterministic metrics: automating the checks you would otherwise do by hand

Article15 JAN 2026

Deterministic metrics: automating the checks you would otherwise do by hand

Small pieces of code that run on every answer and check for something specific — length, format, language, JSON schema, expected items. How metrics measure (not enforce) and connect back to error analysis.

Madalina Turlea

Prompt Engineering Techniques: Part 4

Article09 JAN 2026

Prompt Engineering Techniques: Part 4

From craft to system: how to test prompts rigorously, compare approaches on real data, and build repeatable processes for prompts running inside a product or workflow.

Maria-Fontica Marinescu

Prompt Engineering Techniques: Part 3

Article07 JAN 2026

Prompt Engineering Techniques: Part 3

Validation-side techniques for verifying AI outputs before you act on them: role prompting for perspective-shifting, self-consistency checks, and critical analysis prompts.

Maria-Fontica Marinescu

3 mistakes that make your AI feature a silent churn machine

Article15 OCT 2025

3 mistakes that make your AI feature a silent churn machine

Most AI features don't fail loudly. They quietly underperform while users quietly leave. Here are the three mistakes driving silent churn.

Madalina Turlea

The 4 most expensive AI evaluation mistakes (and the tender that died)

Article15 OCT 2025

The 4 most expensive AI evaluation mistakes (and the tender that died)

Four AI evaluation mistakes that cost product teams real money — from blown model selection to a million-euro tender lost to silent failures.

Madalina Turlea

5 AI testing mistakes that will cost you under the EU AI Act

Article15 OCT 2025

5 AI testing mistakes that will cost you under the EU AI Act

The EU AI Act demands documentation, traceability, and risk management. Most product teams are still shipping AI on vibes. Here are five mistakes to fix now.

Madalina Turlea

4 mistakes that turn a mediocre AI feature into a user trust killer

Article15 OCT 2025

4 mistakes that turn a mediocre AI feature into a user trust killer

Most AI features don't fail loudly. They quietly erode trust. Here are four mistakes product teams make that turn mediocre AI into a reason users leave.

Madalina Turlea

The 3 prompt testing mistakes most teams don't know they're making

Article15 OCT 2025

The 3 prompt testing mistakes most teams don't know they're making

Most teams test prompts wrong without realizing it. Here are three specific mistakes that silently kill AI feature quality — and how to fix each one.

Madalina Turlea

5 mistakes product teams make when shipping AI features

Article13 OCT 2025

5 mistakes product teams make when shipping AI features

Product teams keep making the same five AI shipping mistakes. Here's what they are, what they cost, and how to fix each one before users find out.

Madalina Turlea

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