How to evaluate LLMs: Full LLM evaluation guide

May 20, 2025

How to evaluate LLMs: Full LLM Evaluation Guide

All you need to know if you are just getting started and are confused.

Building with LLMs feels like trying to ship a product on quicksand. Same input, different output. Hallucinations. Inconsistency. And don’t even get me started on jailbreaks.

This post is a ractical guide to making LLMs more reliable, based on a talk I recently gave at PyData London. I’ll walk you through a framework we use at ParsLabs to go from experiments to production. It’s been shaped by 100+ real-world AI automation projects, and yes, it’s still evolving, just like everything in this space.

Let’s dive in.

Why this matters

A few weeks ago, I shared a list of 61 LLM eval tools on LinkedIn. That post blew up — 40,000+ views, 200+ submissions.

Here’s what I learned:

  • Most teams are still not doing any LLM evaluation

  • Many rely on “vibe checks” (just reading the output and going by gut)

  • A surprising number are writing custom Python eval code instead of using libraries

  • Very few use structured, repeatable frameworks

This tells me one thing: we’re still winging it. We need a system.

The risks of skipping evaluation

If you’re not evaluating LLM output, you’re launching blind. Here’s what can go wrong:

  • Hallucinations → confident lies that waste user time and destroy trust

  • Inconsistent output → users get confused, support tickets pile up

  • Harmful responses → bias, stereotypes, misinformation

  • Jailbreaks → attackers bypass safety filters

  • Data/PII leaks → legal and reputational nightmares

LLMs are not deterministic. That’s the core problem. You can’t “hope” your app behaves. You need systems that actively test, monitor, and catch issues — before users do.

The 3-Stage Framework: Experiment → Monitor → Improve

Let’s break it down.

1. Experiment


1.1. Define what “good” looks like

Before writing prompts, define your test set. It should:

  • Cover happy paths, edge cases, sensitive topics, jailbreaking attempts

  • Include reference answers or custom criteria

  • Be rooted in real examples — not made-up ones

To make sure your test set represents real world data:

  • Invite subject matter experts to give you input on what good means in your domain

  • Consult existing documentation to find hints, e.g. customer support scripts, HR docs, onboarding documents

  • Review past conversations or production logs

Tip: Build tests before prompts. Yes, even for LLMs.

1.2. Choose your metrics

There’s no universal “best” metric — choose what fits your use case. A few categories:

  • Simple checks: regex, syntax match

  • Statistical metrics: precision, recall, BLEU, ROUGE

  • ML-based: BERTScore, sentiment analysis

  • LLM-as-a-judge: use one LLM to evaluate another’s output against custom criteria

Example: “Was this response polite?” → Yes/No + explanation.

Start simple. Select just a few metrics that make sense to you and go test it. You can evolve over time.

1.3. Run experiments

When testing:

  • Start with vibe check

  • Then run automated evals

  • Finish with manual review (to verify your metrics make sense)

Use tools like:

  • Prompt playgrounds with version control (e.g., Agenta, LangWatch, Langfuse, e.t.c.)

  • Unit, integration, and end-to-end tests

  • LLM test libraries (e.g., DeepEval, RAGAs)

2. Monitor (in production)

2.1. Add real-time guardrails

Two types:

  • Input guardrails: catch bad user prompts early (e.g., profanity, jailbreak attempts)

  • Output guardrails: catch bad generations (e.g., hallucinations, unsafe content)

Don’t overdo real-time filters — they add latency. Only use for high-risk apps. Instead, bake most of your checks into the test set.

Tools:

  • OpenAI and AWS moderation APIs

  • Guardrails libraries (e.g., Guardrails AI)

  • Custom LLM-as-a-judge filters

Always check for hallucinations and syntax issues before showing output.

2.2. Log everything

Track:

  • Input, output, and context

  • Prompt version and model

  • Any fallback or retry logic

  • User feedback (explicit or implicit)

Without logs, you can’t fix what breaks.

2.3. Set up alerts

Don’t wait for users to tell you it’s broken. Use alerts for:

  • No-response loops

  • Dangerous output

  • Repeated fallback events

3. Improve (post-production)

Logs are gold. Review them to:

  • Spot failures and blind spots

  • Update your test set

  • Track custom KPIs

  • Debug regressions after model or prompt changes

Reminder: your test set is never done. It evolves with your product.

Final takeaways

As developers, it's our responsibility to make sure LLM outputs are reliable.

Here’s what to remember:

  • Start with tests, not prompts

  • Measure what matters for your use case

  • Use automation to scale judgment, not replace it

  • Log and analyze everything in production

  • Improve constantly — your test set is a living thing

And most importantly: reliability is your job, not OpenAI’s. If your product misbehaves, users will blame you, not the model vendor.

Own it.