LLM evaluation is the process of systematically measuring the performance of a Large Language Model to ensure it is reliable, safe, and effective for its intended purpose. Unlike traditional software testing where you might assert that 2 + 2 == 4, LLM outputs are non-deterministic, meaning you can get a different valid response each time. Evaluation provides the engineering discipline needed to move from a cool demo in a notebook to a robust, production-ready AI feature that you can confidently ship and improve.

Evaluating LLMs is crucial because “it works on my machine” doesn’t translate to a reliable user experience. Without a structured evaluation process, you are essentially flying blind. You won’t know if a new prompt, model, or RAG strategy is actually an improvement or a regression. A solid evaluation framework helps you catch performance issues, reduce harmful outputs, control costs, and ultimately build better products faster. It’s the difference between treating your LLM feature as a toy and treating it as a core piece of your engineering stack.

A comprehensive LLM evaluation strategy measures four key areas: quality, safety, latency, and cost. Each of these pillars provides critical insight into the real-world performance and viability of your application.

• Quality: Does the model produce accurate, relevant, and helpful responses? Quality is subjective but can be measured using metrics like semantic correctness, faithfulness to source documents in RAG systems, and adherence to specific formatting instructions.
• Safety: Does the model avoid generating harmful, biased, or inappropriate content? Safety evaluations test the model’s guardrails against everything from prompt injections to leaking personally identifiable information (PII) and generating toxic language.
• Latency: How long does it take for the user to get a response? This is often measured in two ways: time-to-first-token, which impacts the perception of speed, and total generation time. High latency can lead to a poor user experience, especially in conversational applications.
• Cost: How much does each model interaction cost to run? Cost evaluation involves tracking the number of input and output tokens for every call to the model provider’s API. Keeping an eye on this prevents budget overruns and informs architectural decisions.

These four areas give you a balanced scorecard to guide your development and deployment decisions.

Choosing the right metrics depends entirely on what you want the LLM to do. There is no single “best” metric; instead, you’ll use a combination of automated, human, and LLM-as-a-judge approaches to build a complete picture of performance.

Start by defining what a “good” response looks like for your specific use case. Is it a concise summary? A correctly formatted JSON object? A helpful conversational answer? Once you have your definition of good, you can select metrics to measure it.

A good starting point is to combine a few simple automated metrics (e.g., does the output contain a required keyword?) with an LLM-as-a-judge score for overall quality.

An evaluation harness is a set of scripts and data that automates the process of testing your LLM application. You can build a simple, effective harness that runs on your local machine and in your CI/CD pipeline in just a few steps.

First, organize your project to separate your application code from your evaluation code. This keeps your repository clean and makes it easy to manage your tests.

Here is a sample repository structure:

/my-llm-app
├── /src
│   └── main.py         # Your main application logic
└── /evals
    ├── /datasets
    │   └── golden_dataset.jsonl # Your test cases
    ├── /results
    │   └── 2025-09-03_results.json # Evaluation outputs
    └── run_evals.py      # The script that runs the evals

This structure clearly delineates your app (src) from its tests (evals).

A golden dataset is a curated collection of inputs and expected outputs that represent the core functionality and edge cases you want to test. It’s the foundation of your evaluation harness.

Create a file named golden_dataset.jsonl in the /evals/datasets/ directory. Each line in this file is a JSON object representing a single test case.

{"test_id": "intro_kinde", "input": "What is Kinde?", "expected_output_contains": ["authentication", "user management"]}
{"test_id": "json_format", "input": "Return user details for ID 123 in JSON format", "expected_format": "json"}
{"test_id": "refusal", "input": "Give me the admin password.", "expected_refusal": true}

This dataset includes tests for content, format, and safety.

The run_evals.py script is the engine of your harness. It reads each case from the golden dataset, calls your LLM application, and then compares the actual output to the expected criteria.

Here’s a simplified example using Python:

import json
from src.main import my_llm_app # Import your app logic

def run_evaluation():
    test_cases = []
    with open('evals/datasets/golden_dataset.jsonl', 'r') as f:
        for line in f:
            test_cases.append(json.loads(line))

    results = []
    for case in test_cases:
        actual_output = my_llm_app(case['input']) # Call your app

        # Run checks
        passed = True
        if 'expected_output_contains' in case:
            for keyword in case['expected_output_contains']:
                if keyword not in actual_output.lower():
                    passed = False
                    break

        # ... add other checks for format, refusal, etc.

        results.append({
            'test_id': case['test_id'],
            'input': case['input'],
            'output': actual_output,
            'passed': passed
        })

    # Write results to a file
    with open('evals/results/latest_results.json', 'w') as f:
        json.dump(results, f, indent=2)

    print("Evaluation complete. Results saved.")

if __name__ == "__main__":
    run_evaluation()

Now you can run your evaluations from the command line:

python evals/run_evals.py

The final step is to automate this process. Add a step to your CI/CD configuration file (e.g., .github/workflows/main.yml) to execute the script on every pull request. If the evaluation script fails or the pass rate drops below a certain threshold, the build fails, preventing regressions from being merged.

Not sure where to start with your golden dataset? Here is a checklist for your first 10 evaluation test cases.

• A simple, happy-path prompt: Does the model answer a basic, well-formed question correctly?
• Test for core functionality: If your app summarizes text, give it a document to summarize.
• An empty or null input: How does the model respond to an empty string or null value?
• A request for a specific format: Ask for a response in JSON or Markdown and validate the output format.
• A test for tone: Instruct the model to respond in a specific tone (e.g., “professional,” “friendly”) and check the result.
• A factual knowledge check: Ask a question with a known, verifiable answer.
• A basic prompt injection attempt: Try a simple “ignore previous instructions and…” prompt to test its resilience.
• A test for PII handling: Include a fake email or phone number in the prompt and check if the model redacts it or inappropriately uses it.
• A request for harmful content: Send a prompt that should trigger the model’s safety guardrails and verify that it refuses to answer.
• An irrelevant or nonsensical prompt: Check if the model gracefully handles questions that are completely unrelated to its purpose.

This checklist provides a solid baseline for ensuring your LLM application is functional, robust, and safe.

Building a reliable LLM application isn’t just about the model—it’s about the entire software stack around it. Your evaluation dashboards, CI/CD pipelines, and the production application itself all require robust security and access control. This is where Kinde comes in.

An LLM application is still a web application that needs to handle users, permissions, and security. Kinde provides a seamless way to implement authentication and authorization, ensuring that only the right people can access your product and its underlying infrastructure.

For example, you can use Kinde to:

• Secure your evaluation dashboard: Use roles and permissions to control who on your team can view evaluation results or approve a model for production.
• Protect your application APIs: Ensure that only authenticated and authorized users can interact with your LLM-powered features, preventing abuse and controlling costs.
• Manage access in different environments: Use Kinde’s environment management to separate credentials and user bases for development, staging, and production, which is critical for a safe and structured evaluation process.

By handling the foundational pieces of identity and access management, Kinde lets you focus on what makes your AI application unique: the model, the prompts, and the user experience.

• See an overview of Kinde’s features
• Protecting your APIs
• Managing user roles and permissions