How to evaluate LLMs: Full LLM evaluation guide

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 practical 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: we got 40,000+ views, 200+ form 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: everyone is still trying to figure out how to properly evaluate LLMs. Let's try to figure this out together!

The risks of skipping evaluation

If you’re not evaluating LLM output, you have no way to control the output. 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 non-deterministic by nature, and that’s the core problem. This means that for the exact same input you can get different output. If you want to build a reliable AI product, you need a way to test, monitor, and catch issues, before your 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.

Select just a few metrics that make sense to you and go test it. You can add extra metrics over time, if you need to. Start simple.

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 analyse everything in production

• Improve constantly — your test set is a living thing

And most importantly: it's our responsibility as developers to make sure our apps are reliable and produce consistent output. If our product misbehaves, users will blame us, not the model vendor.

So we need to own it.