Why Evaluating Your LLM Agent Is So Important (And How To Do It Right)

Greetings, traveller from beyond the fog. I am Moji. I offer you an accord. Have you heard of the LLM Evaluation? They serve the Large Language Models, offering guidance and aid to the Tarnished. But you, I am afraid, are maidenless. I can play the role of maiden, turning runes into strength to aid you in your search for the quality of your agent. You need only take the link of this blog with you to the foot of your notes. Then it's settled. Summon me by grace to turn runes into strength.

Sorry, sorry, I played too much Elden Ring recently. Let's get back on track.

Why Should You Care About Evaluating Your LLM Agent?

Large Language Model (LLM) agents are incredible tools. They can plan, reason, solve problems, retrieve data, summarize, and even hold conversations. But here's the catch: just because your LLM agent responds, doesn't mean it's doing what you want.

Evaluation is the process that ensures your LLM is actually useful, reliable, and truthful. Without it, you're essentially blindfolded, tossing prompts into a magic box and hoping the output makes sense.

What Happens Without Evaluation? (Real-Life Examples)

Let’s say you’re building an AI assistant for a legal helpdesk:

Scenario 1: Answer Mismatch
The user asks: “Can I break my lease early in California?”
Your agent replies with a general lease clause that’s accurate, but in New York.
Scenario 2: Hallucination
Your agent claims there's a "Section 14B" in a contract template that doesn’t exist. The user acts on it, confusion ensues.
Scenario 3: Multiple Intents, One Answer
A user says: “Can I get a refund and cancel my account?”
The agent only responds about the refund, ignoring cancellation.

These are just a few of many ways your model can go off the rails. Without evaluation, you might never even realize it.

So, What Should You Actually Measure?

Depending on your use case, evaluation can take many forms, but here are five core aspects you should always consider:

Intent Match
Did the model understand the question/task the user actually asked?
Factual Accuracy (Truthfulness)
Is the response correct, verifiable, and grounded in your dataset or real-world facts?
Relevance
Did the response actually answer the question or go off-topic?
Clarity and Format
Is the answer easy to understand, free of jargon, and in the right format?
Tone and Persona Alignment
Does it sound like your brand? Should it be casual, formal, funny?

But How Do You Evaluate It? (Here’s the Solution)

Good news, fellow adventurer, there is a sacred rune for this quest.

You can evaluate LLMs using a combination of automated checks, LLM-as-a-judge, and human review:

✅ Log user queries and LLM responses – always.
✅ Use LLMs themselves to self-evaluate. Ask them: “Was this response truthful?” or “Did this match the user’s intent?”
✅ Create evaluation scripts that check outputs against ground-truth answers from your Q&A database.
✅ Add binary or scaled scoring (0/1 or 1–5) for things like answer correctness, tone, or format.
✅ Visualize metrics over time (e.g., how many hallucinations per 100 responses).

Here’s a basic evaluation setup to start with:

{
  "user_query": "Can I change my shipping address?",
  "llm_response": "Sure! You can change it in your profile settings.",
  "ground_truth": "Users can change their shipping address in the Orders section before shipment.",
  "question_match_score": 1,
  "truthfulness_score": 0
}

Over time, you’ll gain insights into where your agent shines, and where it needs a new build (or perhaps just a better prompt).

Final Words, Don’t Stay Maidenless

Evaluating your LLM agent is not just about metrics, it's about trust. It’s about giving your users a reliable companion in their journey, not a capricious mimic. And once you have a solid evaluation pipeline, improving your model becomes less of a guessing game and more of a strategy.

So take up the accord, Tarnished. Run your evaluations. Polish your prompts. Tune your agents.

And may your LLM never hallucinate again.

Summon me by grace (or just subscribe) for more guides on building reliable AI agents.

Why Evaluating Your LLM Agent Is So Important

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