Webinar recap: LLM Evaluations: Best Practices, LLM Eval types & real-world insights

Manouk Draisma

In an era where generative AI is reshaping how software is built, ensuring your large language model (LLM) works reliably and safely is no longer a nice-to-have—it's mission-critical. Yet, 85% of AI projects still fail to meet their goals, often due to poor evaluation strategies. Understanding how to evaluate LLMs effectively is one of the most powerful levers in improving your product and user experience. This post breaks down the core components of LLM evaluation—from datasets to evaluators—explores best practices, and shows how real-world teams are applying these strategies to ensure AI actually delivers.

What is LLM Evaluation?

LLM evaluation is the process of testing, validating, and improving large language model systems through structured assessment methods. It’s more than just checking outputs—it’s about testing the entire system in context.

At the heart of every AI evaluation are three key components:

Datasets: Represent real-world tasks and user goals. These can be synthetic, hand-crafted, or sampled from production logs.
Executors: The full AI pipeline under test, from input handling to the final response. This ensures evaluations capture real agent behavior, not just isolated completions.
Evaluators: These judge quality—whether humans, hard-coded rules, or another LLM acting as a judge. They assess correctness, tone, compliance, creativity, and more.

Why LLM evaluations matters

Without structured evaluations, LLMs might look fine in a sandbox—but fail when scaled. Thorough evaluation reveals blind spots early, reduces regressions, and builds trust in AI systems.

Best Practices in AI & LLM Evaluation

LLM evaluations aren’t just about measuring performance—they're part of the development loop. Here are four essential practices to get it right:

1. Start Small

Use a handful of examples to test new prompts, workflows, or agent behaviors. Small-scale, high-quality test cases offer fast feedback and help you iterate before scaling.

2. Examine Data Closely

Look at real interactions. Analyzing logs helps you uncover how users actually interact with your LLM—revealing edge cases, failure points, and unexpected usage patterns.

3. Collaborate with Domain Experts

Involve product owners, support agents, legal advisors, or medical experts. Their insights make evaluations more relevant, especially when quality depends on context (e.g., empathy in coaching, tone in customer support, or terminology in finance).

4. Deeply Consider Quality Metrics

Define what “good” means for your use case. It might be accuracy for one, tone compliance for another, or goal achievement for agents. Let your evaluation metrics reflect real-world success.

Types of Evaluations: Offline vs Online

Evaluations happen at multiple stages of the development lifecycle. Understanding both offline and online approaches helps you catch problems before and after deployment.

Offline Evaluations

Used during development, these help you catch issues before pushing to production:

Notebook experiments: Run test prompts and responses under different conditions.
Prompt A/B tests: Compare approaches to see which is more effective.
Dataset evaluations: Run agents against curated tasks to benchmark performance.

Offline evaluations give a stable foundation before users interact with your model.

Online Evaluations

Once in production, you need continuous feedback:

Live traffic monitoring: Sample and score real usage.
Behavioral alerts: Detect drift, bias, or jailbreak attempts.
Guardrails and custom tests: Monitor for policy violations (e.g., PII leaks, hallucinations, tone mismatches).

Together, offline and online evaluations form a feedback loop that drives reliable LLM performance over time.

Evaluation Techniques: From Golden Answers to AI-as-Judge

1. Expected Answers ("Golden Responses")

This classic method compares AI responses to known correct outputs. Great for fact-based tasks:

Is the date correct?
Did the model extract the right fields?
Does the summary match the original intent?

It's objective, fast to run, and ideal for regression testing.

2. LLM-as-Judge

But not every task has a single “correct” answer. For subjective or creative tasks—like writing, coaching, or customer support—LLMs can be used to judge other LLMs.

This method assesses:

Creativity and originality
Adherence to tone/style
Achievement of high-level goals (e.g., resolve user complaint, guide reflection, match brand voice)

Instead of binary scores, you get nuanced, multi-dimensional feedback.

3. Custom Rules & Task-Based Goals

In many real-world setups, the “right” evaluation involves custom rubrics:

Did the bot escalate when needed?
Did it avoid a leading question?
Was the output vegetarian and nutritionally balanced?

These bespoke checks reflect actual business objectives—not just model performance.

Real-World Examples of LLM Evaluation

✅ Evaluating Customer Support Bots

A global fintech company used LangWatch to evaluate its AI chatbot. Key practices:

Simulate real tickets using past customer queries
Track KPIs like resolution rate, escalation triggers, and tone compliance
Use human feedback and LLM-as-judge scoring to rank completions

Result: Faster iteration cycles and improved CSAT scores across multiple markets.

🧠 Coaching Assistants

An AI-based career coach needs to avoid leading the user and focus on reflective questioning. Evaluation focused on:

Stylistic feedback (Was the tone supportive?)
Dialogue flow (Was the assistant guiding or pushing?)
Behavior guardrails (Did it respect user boundaries?)

A mix of manual checks and prompt-based LLM evaluators provided scalable review.

🍽️ Vegetarian Recipe Generator

A recipe AI had to generate tasty, creative, and compliant outputs. Evaluation included:

Scenario testing with dietary constraints (e.g., no eggs, gluten-free)
Human scoring for creativity, novelty, and ingredient compatibility
Rule-based checks for compliance (no meat, dairy, etc.)

Looking Ahead: The Future of LLM Evaluation

As generative AI gets more powerful, evaluations must evolve too. Emerging trends include:

Multi-agent critique loops where agents evaluate each other
Goal-based evaluation frameworks aligned with full workflows
Dynamic test sets that adapt to user behavior in real-time
Hybrid human-AI review teams to scale oversight

LangWatch is building for this future—combining structured offline evaluations, live observability, and flexible scoring systems into one cohesive platform.

FAQ: LLM Evaluations

What are LLM evaluations?

They’re structured tests to measure how well your large language model performs on real or simulated tasks—evaluating accuracy, safety, tone, and more.

Why is LLM evaluation important?

Because LLMs can fail silently—hallucinating, producing biased content, or missing user expectations. Evaluation helps you catch and fix issues early.

How do I evaluate my prompts?

Start with small examples, compare outputs using golden responses or LLM-as-judge methods, and track performance over time using tools like LangWatch.