AI Evals Explained · Aishwarya Srinivasan
How to Evaluate
AI Agents
A practical guide to AI evals: what they are, how they differ from benchmarks and classic machine learning evaluation, which metrics to track for which task, and the evaluation loop that separates teams shipping production agents from teams shipping demos.
Source: youtube.com/watch?v=_Er8Hao_gmQ · Channel: Aishwarya Srinivasan
Agenda
What We'll Cover
Understanding Evals
- What is an AI eval?
- Evals vs benchmarks vs classic ML evaluation
- The metrics that matter — and the three families
- A worked example: the demo generator
The Practice
- Start with a golden dataset, not a metric list
- Four ways to grade agent outputs
- Evals are a loop, not a report card
- Tools for running and monitoring evals
- Key takeaways
Section 1 · What Is an AI Eval?
Is It Getting Better —
or Did You Just Break It?
An agent is a chain of decisions
Pick a tool → call it → read the result → decide what to do next — many places where things quietly go sideways. When it breaks in production you need to know where and why, not read transcripts one at a time at 2 a.m.
Lab scores run ahead of reality
Research shows roughly a one-third gap between how models score in the lab and how they perform in real, deployed work. Evals are how you close that gap before your users feel it.
Section 2 · Evals vs Benchmarks vs Classic ML
Benchmarks Test the Model.
Evals Test Your System
Benchmarks
A generic test of the model.
The same test applied to every model: SWE-bench for coding, Tau-bench for customer-service agents, Terminal-Bench for command-line work, composite indexes that mash many together. Great for choosing which models to start with.
AI evals
A test of your specific agent.
Your task, your data, your prompts, your users — because the benchmark never saw your data, it can't tell you whether the model works for you.
Use benchmarks to pick a starting model — then verify it with evals on your own task.
Section 2 · Evals vs Benchmarks vs Classic ML (cont.)
From a Multiple-Choice Test
to an Essay
Classic ML evaluation
Grading with an answer key.
Outputs were clean — a category, a number, yes or no. A test set with the right answers made grading objective: accuracy, precision, recall, F1. Everybody agreed on what "correct" meant.
AI evals
Grading without one.
Output is open-ended text or a whole sequence of actions — a hundred good summaries can look completely different from each other. There is no answer key: you need a rubric and somebody to use judgment.
The good news: once the rubric mindset clicks, everything else gets simpler.
Section 3 · The Metrics That Matter
Four Metrics Cover Most Cases
Dozens of metrics feel like alphabet soup — in practice, only a handful are worth reaching for again and again.
Relevance
Did the output actually answer what was asked?
Faithfulness
Also called groundedness: is it true and backed by real data, or is the model making it up? This one is your hallucination check.
Correctness
Did it match the expected answer, when there is one?
Coherence
Does it actually read well?
These four are the top-level metrics — but the right metric for any given evaluation depends entirely on your task.
Section 3 · The Metrics That Matter (cont.)
Match the Metric to the Task
| Task | What you care about | Go-to metrics |
|---|---|---|
| Summarization | Stayed true to the source without inventing · captured the main points · kept it tight | Faithfulness · coverage · conciseness |
| Classification | Output is a clean label — old-school ML again | Accuracy · precision · recall · F1 |
| Translation | Meaning carried over · reads naturally in the other language | BLEU · COMET |
| RAG | Together they catch the two ways RAG breaks: bad retrieval or bad generation | Faithfulness · answer relevance |
Pick the metric after you know the task — never before.
Section 3 · A Quick Pause — Worked Example
One Slash Command → a Full Demo Package
Every weekly demo needs code, a README, a slide deck, a script, and a social post — so Srinivasan built a demo generator that produces all of it from a single slash command in her terminal.
Built on Mistral Vibe
- Terminal-native coding agent powered by Mistral Medium 3.5
- Understands a full codebase; helps write, test, refactor, deploy
- Custom subagents: specialized agents with specific scope, tools, and permissions, delegated on demand
- Custom skills: markdown files with YAML frontmatter declaring the slash command and workflow
The generate-demo skill
- Asks clarifying questions first (built-in multiple-choice)
- Research subagent gathers what is needed
- Coding subagent writes the demo and runs it to confirm it works
- Writer subagent drafts the README and slide outline
- Writer reads a "voice file" of tone rules — output sounds like her, not generic AI copy
What used to take a full afternoon now takes a few minutes — composing specialized agents into one repeatable workflow.
Section 3 · Three Families of Metrics
Alphabet Soup, Sorted Into Three Families
1 · Overlap metrics
BLEU · ROUGE · METEOR
Measure word overlap against a reference answer. Fast, cheap, dead simple — but basically blind to meaning: the same idea in different words can be scored differently.
2 · Semantic metrics
BERTScore · BLEURT · COMET
Compare meaning using embeddings instead of matching words — they handle paraphrasing far better than overlap-based scores.
3 · Model-based metrics
LLM as judge · G-Eval
A model grades outputs against your rubric — the only family flexible enough to scale to open-ended, messy, reference-free agent outputs.
Agent outputs are open-ended and reference-free — so more and more, the thing doing the grading is a model.
Section 4 · Start With a Golden Dataset, Not a Metric List
Dataset First — Metrics Fall Out of It
What a golden dataset is
Not complicated: a handful of example inputs for your task, each paired with what a great output looks like and what a bad output could look like. Include your common cases, your annoying edge cases, and every failure you have already watched blow up — an answer key written for your own task.
Then work backwards
Ask of each example: what would make this output good, and what would make it bad? The quantified ways of judging those answers are your metrics.
Section 5 · Four Ways to Grade Agent Outputs
Choose Your Grader
Human evals
The gold standard.
A domain expert scores outputs against your rubric. Slow, expensive, does not scale — save it for small, careful samples and treat it as the source of truth.
User feedback
Real ground truth.
Thumbs up or down, accept or reject, edits, task completion, repeat visits. Real people using your product — but noisy, and only available after you ship, so it cannot be your safety net.
Programmatic evals
Cheap and instant.
Code checks: valid JSON? the right tool with the right arguments? how long, how much? Run on every single change — but they only work when answers are clearly checkable, which pairs beautifully with classification.
LLM as a judge
Essay grading at scale.
A strong model grades outputs against your rubric — thousands of open-ended answers. The one rule: judge models have biases, so check their grades against a human grader on a sample first.
Section 6 · Evals Are a Loop, Not a Report Card
Run It on Every Change, Forever
Not a one-time check before launch — the same idea as CI/CD: tests run on every change, forever. For AI agents: CI/CD plus continuous evaluation and continuous monitoring.
Section 6 · The Loop in Production
Keep Going — Because Models Shift Under You
Providers can quietly swap models
Your model provider can update the models underneath you — and your agent's behavior can shift overnight without you touching a thing. That is why production monitoring never stops.
Failures flow back into the dataset
Real-world failures should go right back into your golden dataset — and then you run the whole loop again.
Teams that ship
Treat evals like continuous testing: always running, catching problems before users ever see them.
Teams that break
Run evals once at shipping time, then find out about every single bug from angry users.
Section 7 · Tools for Running and Monitoring Evals
Don't Build It All From Scratch
Running the evals
- Promptfoo — test prompts and run evals right from the command line
- RAGAS — RAG metrics straight out of the box
Tracing and monitoring
Watch what is happening in production with LangSmith, Langfuse, Arize, and Braintrust.
Pick the ones that fit your stack, wire them into the loop, and start measuring.
Key Takeaways
The Complete Checklist
The End
Keep Measuring Forever
Evals are what separate demo agents from shipped agents: decide what good means, build the golden dataset, let the metrics fall out, and rerun on every change — because "it works" is only true until you measure it.
Source: "AI Evals Explained | How to evaluate AI Agents?" — Aishwarya Srinivasan (youtube.com/watch?v=_Er8Hao_gmQ)