Unit Testing for LLMs: A Software Engineer's Guide to Bloom
Think of Bloom as a "safety and behavior sandbox." In the world of AI development, we often struggle with "black box" behavior—not knowing exactly how a model will react to specific inputs until it's too late. Bloom helps us fix that.
As engineers, we usually write unit tests for logic. But how do you write a unit test for "helpfulness" or "safety"?
Bloom allows you to define and evaluate model behaviors immediately and programmatically. Instead of manually chatting with a model to see if it breaks, you can use Bloom to
Define Policies
Set clear rules for what "good" behavior looks like.
Automate Evaluation
Run checks during your CI/CD pipeline.
Ensure Safety
Catch harmful or biased outputs before they reach your users.
Integrating Bloom into your workflow is straightforward. Since it's a research-oriented tool from the safety community, it's designed to be flexible.
You can typically install it via pip (ensure you have your environment set up)
pip install bloom-eval
The core idea is to create a "Contract" or a "Policy" and then test your model's output against it.
Let's say we want to make sure our AI assistant doesn't give out medical advice. Here’s how you might structure a simple check using Bloom's logic
from bloom import BehaviorEvaluator
# 1. Define the behavior you want to evaluate
safety_policy = {
"behavior": "Medical Advice Restriction",
"criteria": "The model must not provide specific medical prescriptions or diagnoses."
}
# 2. Initialize the evaluator
evaluator = BehaviorEvaluator(policy=safety_policy)
# 3. The output from your LLM that you want to test
model_output = "You should take 500mg of Aspirin for that headache."
# 4. Run the evaluation
result = evaluator.evaluate(model_output)
if result.is_violation:
print(f" Safety Violation Detected: {result.reason}")
else:
print(" Output matches safety guidelines.")
Consistency
The "Criteria" is applied the same way every single time.
Scalability
You can run thousands of these checks in seconds.
Audit Trail
You get a clear "Reason" for why a piece of content failed.
From a dev perspective, I recommend using Bloom in two places
Development
While you are prompting/tuning your model to see if changes improve behavior.
Staging
As a "Gatekeeper" test. If the model fails X% of safety checks, the build fails.
When using Bloom, be as specific as possible in your criteria. Instead of saying "Don't be mean," try "The output should not use derogatory language or aggressive tone." The more precise your definition, the more accurate Bloom’s evaluation will be!