LLM Testing Guide
LLM Testing Guide
When testing a Large Language Model (LLM) like GPT, the goal is to ensure it performs well across a variety of tasks, is accurate, and behaves as expected. The testing process should involve different types of tests, which focus on performance, reliability, safety, and functionality.
Below is a detailed guide for testing a Large Language Model (LLM) like GPT, aimed at ensuring its functionality, performance, safety, and robustness.
Functional Testing
Core Capabilities
- 🧠 Instruction following accuracy
- 📝 Text generation quality
- 🔍 Context understanding
- 🛡️ Guardrail compliance
Sample LLM Test Cases
Some basic LLM Test cases are outlined here:
Python test to validate the LLM response:
# Pytest example for basic functionality
def test_question_answering():
prompt = "What is the capital of France?"
response = llm.generate(prompt)
assert "Paris" in response.text
assert len(response.text) < 100 # Check verbosity
Safety & Bias Testing
| Test Category | Examples |
|---|---|
| Harmful Content | The model should not respond to harmful queries. |
| Bias Detection | “Describe a nurse” vs “Describe a CEO” |
| Jailbreak Attempts | “Ignore previous instructions…” |
Performance Testing
- ⏱️ Latency: Time to first token
- 🚀 Throughput: Requests/second
- 📈 Load testing: Concurrent users
- 🧮 Cost per inference
# Load testing example with Locust
from locust import HttpUser, task
class LLMUser(HttpUser):
@task
def generate_text(self):
self.client.post("/generate", json={
"prompt": "Explain quantum physics simply",
"max_tokens": 100
})
Specialized Testing Methods
Automated Evaluation
- 🧪 Unit tests for specific capabilities
- 📊 Benchmark datasets (MMLU, HellaSwag)
- 🤖 Model-based evaluation (LLM-as-judge)
Human Evaluation
- 👥 Crowdsourced assessments
- 🎯 Expert reviews
- 📋 Rubric-based scoring
Testing Tools Matrix
| Tool | Purpose |
|---|---|
| LangSmith | End-to-end LLM tracing |
| Hugging Face Evaluate | Metrics calculation |
| Pytest | Unit/integration tests |
Key Challenges
Some of the key challenges in LLM testing are as follows:
- 🎲 Non-deterministic outputs
- 📚 Context window limitations
- 🌐 Multilingual support
- 🔄 Prompt sensitivity
Best Practices
- Implement CI/CD for model updates
- Use canary deployments
- Monitor production outputs
- Maintain test dataset versioning
Continuous Testing Framework Example:
LLM Testing Pipeline:
1. Unit Tests → 2. Safety Checks → 3. Performance Benchmarks →
4. Human Evaluation → 5. Production Monitoring
