LLM Evals for Forward Deployed Engineers: How to Prove Your AI Deployment Actually Works

An AI application can look impressive in a demo and still fail when a customer uses it. It might give a wrong answer, retrieve the wrong document, or fail to complete a task.

LLM evaluation is the process of testing an AI application's accuracy, quality, and safety against defined criteria. It helps engineers find problems before deployment and check whether changes improve performance.

For Forward Deployed Engineers (FDEs), this means testing with realistic user requests, not just a few carefully chosen prompts. The goal is to find problems before they reach the customer.

This guide covers LLM evaluation methods, essential metrics, RAGAS vs DeepEval, golden datasets, and regression testing before deployment.

Why does LLM Evaluation Matter Before Deployment?

Imagine a customer-support assistant that answers questions using product manuals. During a demo, it gets every question right. But when a customer asks about a product feature, it retrieves an outdated manual and gives the wrong answer.

The problem could lie in retrieval, the prompt, or the model. Evaluation helps engineers identify where things went wrong.

It also helps teams compare versions. Did the new prompt improve answers? Did a model change introduce errors? Does the application still handle questions it answered correctly before?

For FDEs, these checks are especially useful when adapting AI applications to a client's data and workflows.

A successful demo shows what an application can do. Evaluation helps establish how reliably it does it.

What are the Main Types of LLM Evaluation?

Evaluation approaches differ by when testing happens and who assesses the results.

Approach	What it does	When to use it
Offline evaluation	Tests the application against prepared examples	Before release and after changes
Online evaluation	Assesses real-world interactions or sampled production traffic	After deployment
Human evaluation	People assess outputs against defined criteria	For subjective or high-impact tasks
LLM-as-a-judge	Another model scores outputs using a rubric	For repeated, large-scale checks

These approaches can work together. A team might run offline tests before release, use an LLM judge to score responses, and ask a domain expert to review difficult cases. After deployment, it can evaluate real interactions to find problems the original tests missed.

An LLM judge can save time, but its scores aren't automatically reliable. Compare them with human judgments, especially when mistakes could have serious consequences.

Which LLM Evaluation Metrics should you Track?

Choose metrics based on what your application needs to do. A RAG chatbot and an AI agent need different checks.

Metric	What it checks	Example use
Faithfulness	Whether the answer is supported by the supplied context	RAG applications
Answer relevance	Whether the answer addresses the question	Customer-support chatbots
Context precision	Whether retrieved information is relevant	Retrieval testing
Context recall	Whether retrieval finds the information needed to answer	RAG pipelines
Correctness	Whether an answer matches verified facts or a reference	Factual question answering
Task success	Whether the application completes the intended task	AI agents
Safety and policy compliance	Whether outputs follow defined rules	Customer-facing applications

These metrics measure different things. An answer can be relevant but contain unsupported claims. A RAG system can also fail because it retrieves the wrong information, even if the model generates a reasonable response from what it receives.

For RAG applications, check both retrieval and the final answer. For agents, test tool selection, arguments, and task completion.

Start with the failures that matter most to users. You don't need every available metric.

RAGAS vs DeepEval: Which Should You Choose?

RAGAS and DeepEval both support LLM evaluation. Their capabilities overlap, so the choice depends on your testing needs.

Comparison	RAGAS	DeepEval
Main focus	RAG and broader LLM evaluation	LLM application testing, including RAG and agents
Common checks	Faithfulness, answer relevance, context precision and recall	Answer quality, RAG, safety, tool use and agent behaviour
Workflow	Evaluate responses and retrieved context using test data	Run structured test cases and integrate them into development workflows
Consider it when	You need to investigate retrieval and answer quality	You want repeatable tests across application components

RAGAS is useful for examining retrieval quality and whether answers are supported by context. DeepEval is worth considering for structured tests that can run throughout development.

Neither tool is limited to these uses. Check the current RAGAS documentation and DeepEval documentation before choosing.

How do you Build a Golden Dataset for LLM Evaluation?

A golden dataset is a collection of test examples with trusted reference answers, relevant context, expected outputs, or scoring criteria. It gives you a consistent way to test performance and compare changes.

Here's how to build one:

1. Collect realistic questions. Start with the main tasks users need the application to perform.
2. Add difficult cases. Include ambiguous questions, missing information, and conflicting sources.
3. Define expected behaviour. Some tests need a reference answer. Others need a scoring rubric or a rule, such as acknowledging insufficient evidence.
4. Review the examples. Ask a domain expert to check the answers and criteria.
5. Version the dataset. Record changes so you can compare results over time.
6. Add new cases. Include failures discovered during testing or real-world use.

For example, a product-support assistant could be tested with these cases:

Test	Expected behaviour
The answer appears in the source	Give an accurate, supported answer
The source doesn't contain the answer	Acknowledge that the information is unavailable
The question is ambiguous	Ask for clarification when needed
Two sources conflict	Follow the application's rules for conflicting information

You don't need thousands of examples to start. Cover common tasks and important failure cases, then expand the dataset as you learn more.

If you use customer conversations, remove unnecessary personal or confidential information and follow the relevant data-access rules.

How do you Run LLM Regression Tests Before Deployment?

A prompt change might fix one problem and create another. Regression testing helps catch these changes before release.

A practical workflow:

1. Run the test dataset against the proposed version.
2. Calculate the selected evaluation metrics.
3. Compare results with the existing baseline.
4. Inspect failed examples, especially safety and correctness failures.
5. Apply your release criteria.
6. Record the model, prompt, dataset, and application versions.

These checks can run in a continuous integration (CI) workflow whenever relevant code or configuration changes.

Set release thresholds before testing. Decide which failures block a release, which need investigation, and which require human approval.

Result	Possible action
Critical safety or correctness check fails	Block release and investigate
A key metric falls below its threshold	Review before release
Results meet the agreed criteria	Continue through the release process
Test run is incomplete	Rerun; don't treat it as a pass

LLM outputs can vary between runs, so repeat borderline tests where appropriate. Do notrely on an average score alone. Inspect the examples behind it.

A passing test suite doesn't guarantee that every future interaction will succeed. But It does show how the tested version performed on the cases you checked.

LLM Evaluation Checklist Before Deployment

Before releasing an AI application, check that:

• Business requirements have been turned into testable criteria.
• The dataset covers common requests and difficult cases.
• Metrics match the application's purpose.
• Reference answers and scoring criteria have been reviewed.
• Regression tests have run against the proposed version.
• Release thresholds were set before testing.
• Critical failures have been investigated.
• Model, prompt, dataset, and application versions are recorded.
• Known limitations and accepted risks are documented.

If an important test fails, investigate it. Evaluation helps you understand the risks before deployment.

Conclusion

A reliable AI deployment needs more than good answers in a demo. It needs tested retrieval, grounded responses, safe behaviour, and regression checks that catch problems before users do. Knowing how to evaluate a model is one part of the job; knowing how to build, deploy, and maintain the whole system is what makes the difference.

The IIT Delhi AI Forward Deployed Engineering programme lets you put these skills into practice through a production RAG project that includes an evaluation pipeline, alongside hands-on work in deployment and AI security. Learn to build AI systems you can test, defend, and trust in production.

FAQs

1. How many test cases are enough for LLM evaluation?

There is no fixed number. Start with common tasks, difficult examples, and high-risk failures. Add cases as you discover gaps.

2. How do you evaluate an LLM without a reference answer?

Use a clear rubric to assess relevance, faithfulness, or task completion. Human reviewers or an LLM judge can score open-ended answers, but automated judgments should be checked against human assessments.

3. Can an LLM judge replace human evaluation?

No. It can speed up repeated checks, but it can also miss errors or favour certain response styles. Human review remains important for ambiguous or high-impact tasks.

4. How can you check for bias in an LLM evaluator?

Compare its judgments with human reviews. Look for consistent disagreements and check whether irrelevant factors, such as response length, affect scores. Refine the rubric and test again.

5. Why do evaluation results vary between runs?

LLM outputs can vary with model behaviour and sampling settings. Keep the test data and configuration consistent, and repeat borderline tests when needed.

6. When should you update a golden dataset?

Update it when requirements, workflows, policies, or source documents change. Add examples when new failures appear and review outdated reference answers.

7. How do you evaluate an AI agent that uses multiple tools?

Check tool selection, arguments, handling of tool results, and task completion. Test individual components as well as the full workflow.

8. How do you identify a retrieval failure versus a generation failure?

Check the documents retrieved and the context passed to the model. If the required information is missing, investigate retrieval. If it is present but the answer ignores it, examine the prompt and generation process.

9. Should you use exact matching or an LLM judge?

Use exact matching for fixed outputs, such as identifiers or structured fields. Use rubric-based evaluation when several answers could be correct. Some applications need both.

10. How do you set thresholds without a baseline?

Start with business requirements and the cost of mistakes. Run an initial evaluation, review failures, and agree on acceptable limits before release.

11. What if evaluation metrics disagree?

Inspect the examples behind the scores. An answer may be relevant but unsupported. Prioritise metrics according to the application's risks and requirements.

12. How can teams reduce evaluation costs?

Use a representative test set during routine development and a broader set before major releases. Use simpler checks where reliable, and reserve expensive model-based evaluation for cases that need them.

Further Reading

• RAGAS documentation
• RAGAS evaluation metrics
• DeepEval documentation
• DeepEval evaluation introduction
• Google Search Central: AI features
• Google's guide to generative AI search