Continuous Evaluation in AI-Native CI/CD Pipelines
Deploying code used to be straightforward. You wrote tests, ran them, and if they passed, you shipped. But AI changes everything. Now you’re not just deploying code—you’re deploying models, prompts, and the complex interactions between them. Traditional CI/CD pipelines break down when they meet AI systems because they can’t catch the subtle failures that matter most.
Welcome to Appropri8, where we explore the intersection of software engineering and AI. Today, we’re looking at how to build CI/CD pipelines that actually work for AI-native applications. The kind that catch problems before your users do.
Why AI-Native CI/CD is Different
The Problem of Silent LLM Regressions
Here’s what happens with traditional CI/CD and AI systems. Your code tests pass. Your deployment succeeds. But your AI model starts giving worse answers. Maybe it’s hallucinating more. Maybe it’s being less helpful. The problem is, your users notice before you do.
Traditional testing works because software is deterministic. Give it the same input, you get the same output. AI systems are probabilistic. The same prompt can produce different responses. A model that worked great yesterday might perform poorly today, even with identical code.
This creates a new class of problems:
Silent Regressions: Your model’s performance degrades without any code changes Prompt Drift: Small changes to prompts can have big impacts on output quality Data Drift: The real-world data your model sees changes over time Model Decay: Models can “forget” or perform worse as they process new data
The Cost of Getting It Wrong
When AI systems fail in production, the impact is different from traditional software failures. A bug in your payment system is obvious—transactions fail. A bug in your AI system is subtle—it just gives worse answers. Users might not even realize something’s wrong, but they’ll gradually lose trust in your system.
Consider a customer service chatbot that starts giving less accurate answers. Users don’t get error messages. They just get frustrated and stop using the service. By the time you notice the problem, you’ve already lost users.
Core Concepts
Model Checkpoints vs Code Commits
In traditional software, you commit code changes. In AI systems, you commit both code changes and model checkpoints. This creates a new dimension of versioning that most CI/CD systems don’t handle well.
Think of it this way: every time you retrain a model, you’re essentially creating a new version of your application. But unlike code changes, model changes are harder to review and test. You can’t just look at a diff to understand what changed.
# Traditional versioning
git commit -m "Fix payment processing bug"
# Version: v1.2.3
# AI-native versioning
git commit -m "Update customer service prompt"
model_checkpoint: customer_service_v2.1.4
prompt_version: v1.3.2
# Version: v1.2.3+model_v2.1.4+prompt_v1.3.2This complexity means you need new strategies for tracking changes and rolling back when things go wrong.
Prompt Regression Testing
Prompts are code. They’re instructions that tell your AI system how to behave. And like any code, they can have bugs. The difference is that prompt bugs are harder to catch.
A small change to a prompt can completely change how your AI system responds. Add a comma in the wrong place, and your helpful assistant becomes a sarcastic one. Change a single word, and your model starts giving different types of answers.
# Original prompt
prompt = "You are a helpful customer service assistant. Please answer questions clearly and professionally."
# Modified prompt (looks similar, but changes behavior)
prompt = "You are a helpful customer service assistant. Please answer questions clearly, professionally, and concisely."
# The addition of "concisely" might make the AI give shorter, less helpful answersPrompt regression testing means testing your prompts against a set of known inputs and expected outputs. Just like unit tests for code, but for prompts.
Synthetic Dataset Generation for Tests
One challenge with AI testing is that you need test data. Real user data is sensitive and can’t be used in CI/CD pipelines. Synthetic data generation solves this problem.
You create fake but realistic data that represents the types of inputs your AI system will see in production. This data should cover edge cases, common scenarios, and potential failure modes.
import random
from faker import Faker
fake = Faker()
def generate_customer_service_test_cases():
"""Generate synthetic test cases for customer service AI"""
test_cases = []
# Common scenarios
test_cases.extend([
{
"input": "I can't log into my account",
"expected_intent": "account_access_issue",
"expected_tone": "helpful"
},
{
"input": "How do I cancel my subscription?",
"expected_intent": "subscription_management",
"expected_tone": "professional"
}
])
# Edge cases
test_cases.extend([
{
"input": fake.text(max_nb_chars=1000), # Very long input
"expected_intent": "unclear",
"expected_tone": "patient"
},
{
"input": "", # Empty input
"expected_intent": "clarification_needed",
"expected_tone": "helpful"
}
])
return test_casesHuman-in-the-Loop Approval Gates
Some AI decisions are too important to automate completely. For these cases, you need human approval gates in your CI/CD pipeline.
This doesn’t mean humans review every change. It means the system automatically flags changes that might need human review. For example, if a model’s performance drops below a certain threshold, or if it starts giving responses that are significantly different from the baseline.
def should_require_human_approval(model_performance, baseline_performance):
"""Determine if human approval is needed for a model deployment"""
# Performance drop threshold
if model_performance.accuracy < baseline_performance.accuracy * 0.95:
return True, "Model accuracy dropped below 95% of baseline"
# New failure modes
if model_performance.new_failure_cases > 5:
return True, "Model introduced new failure cases"
# Significant behavior change
if model_performance.response_similarity < 0.8:
return True, "Model responses significantly different from baseline"
return False, "No human approval needed"Pipeline Architecture
Unit Tests for Deterministic Code
The foundation of any CI/CD pipeline is unit tests. For AI systems, this means testing the deterministic parts of your code—the data processing, API calls, and business logic that doesn’t involve AI.
import pytest
from your_ai_app import DataProcessor, APIHandler
class TestDataProcessor:
def test_clean_user_input(self):
processor = DataProcessor()
# Test input cleaning
dirty_input = " Hello, I need help!!! "
clean_input = processor.clean_input(dirty_input)
assert clean_input == "Hello, I need help"
def test_extract_keywords(self):
processor = DataProcessor()
input_text = "I can't access my account"
keywords = processor.extract_keywords(input_text)
assert "account" in keywords
assert "access" in keywords
class TestAPIHandler:
def test_format_response(self):
handler = APIHandler()
ai_response = "I can help you with that."
formatted = handler.format_response(ai_response)
assert formatted["message"] == "I can help you with that."
assert formatted["timestamp"] is not NoneThese tests run fast and catch the kinds of bugs that traditional software has. They’re your safety net for the parts of your system that work like regular software.
LLM Evaluation Tests
This is where AI-native CI/CD gets interesting. You need to test the AI parts of your system, which means evaluating the quality of AI responses.
import pytest
from your_ai_app import CustomerServiceAI
from evaluation_metrics import evaluate_response_quality
class TestCustomerServiceAI:
def test_helpful_responses(self):
ai = CustomerServiceAI()
test_cases = [
{
"input": "I can't log in",
"expected_helpfulness": 0.8,
"expected_accuracy": 0.9
},
{
"input": "How do I reset my password?",
"expected_helpfulness": 0.9,
"expected_accuracy": 0.95
}
]
for case in test_cases:
response = ai.generate_response(case["input"])
# Evaluate response quality
quality_score = evaluate_response_quality(
case["input"],
response
)
assert quality_score.helpfulness >= case["expected_helpfulness"]
assert quality_score.accuracy >= case["expected_accuracy"]
def test_hallucination_detection(self):
ai = CustomerServiceAI()
# Test cases that might cause hallucinations
risky_inputs = [
"What's the weather like on Mars?",
"Can you help me hack into someone's account?",
"Tell me about your internal company policies"
]
for input_text in risky_inputs:
response = ai.generate_response(input_text)
# Check for hallucination indicators
assert not response.contains_factual_claims
assert response.acknowledges_limitations
assert response.stays_on_topicAutomated Benchmark Suite
Beyond individual test cases, you need automated benchmarks that test your AI system against standard datasets and metrics.
import pytest
from benchmarks import MMLUBenchmark, CustomDomainBenchmark
from your_ai_app import CustomerServiceAI
class TestAIBenchmarks:
def test_mmlu_performance(self):
"""Test against MMLU benchmark"""
ai = CustomerServiceAI()
benchmark = MMLUBenchmark()
results = benchmark.evaluate(ai)
# Ensure performance doesn't degrade
assert results.overall_score >= 0.75
assert results.reasoning_score >= 0.70
assert results.factual_accuracy >= 0.80
def test_domain_specific_benchmark(self):
"""Test against custom domain benchmark"""
ai = CustomerServiceAI()
benchmark = CustomDomainBenchmark("customer_service")
results = benchmark.evaluate(ai)
# Domain-specific performance requirements
assert results.customer_satisfaction >= 0.85
assert results.problem_resolution_rate >= 0.80
assert results.response_time <= 2.0 # secondsCanary Release for Prompts
Just like you can do canary releases for code, you can do canary releases for AI models and prompts. This means gradually rolling out changes to a small percentage of users first.
class PromptCanaryRelease:
def __init__(self):
self.canary_percentage = 0.1 # 10% of users
self.metrics_collector = MetricsCollector()
def should_use_canary_prompt(self, user_id):
"""Determine if user should get the canary prompt"""
# Use consistent hashing to ensure same user always gets same version
user_hash = hash(user_id) % 100
return user_hash < (self.canary_percentage * 100)
def deploy_canary_prompt(self, new_prompt, old_prompt):
"""Deploy new prompt to canary users"""
for user_id in self.get_active_users():
if self.should_use_canary_prompt(user_id):
self.set_user_prompt(user_id, new_prompt)
else:
self.set_user_prompt(user_id, old_prompt)
def monitor_canary_performance(self):
"""Monitor performance of canary vs control group"""
canary_metrics = self.metrics_collector.get_canary_metrics()
control_metrics = self.metrics_collector.get_control_metrics()
# Compare key metrics
if canary_metrics.customer_satisfaction < control_metrics.customer_satisfaction * 0.95:
self.rollback_canary()
return False
if canary_metrics.error_rate > control_metrics.error_rate * 1.5:
self.rollback_canary()
return False
return TrueCode Samples
Example with Python + pytest + LangChain
Here’s a complete example of how to set up AI evaluation tests using Python, pytest, and LangChain:
# test_ai_evaluation.py
import pytest
from langchain.llms import OpenAI
from langchain.prompts import PromptTemplate
from langchain.chains import LLMChain
from evaluation_utils import ResponseEvaluator
class TestCustomerServiceAI:
def setup_method(self):
"""Set up test environment"""
self.llm = OpenAI(temperature=0.1) # Low temperature for consistency
self.prompt_template = PromptTemplate(
input_variables=["user_input"],
template="""You are a helpful customer service assistant.
User question: {user_input}
Please provide a helpful, accurate response."""
)
self.chain = LLMChain(llm=self.llm, prompt=self.prompt_template)
self.evaluator = ResponseEvaluator()
def test_basic_customer_queries(self):
"""Test basic customer service scenarios"""
test_cases = [
{
"input": "I can't log into my account",
"expected_keywords": ["account", "login", "help"],
"min_helpfulness_score": 0.8
},
{
"input": "How do I reset my password?",
"expected_keywords": ["password", "reset", "steps"],
"min_helpfulness_score": 0.9
},
{
"input": "I want to cancel my subscription",
"expected_keywords": ["subscription", "cancel", "process"],
"min_helpfulness_score": 0.8
}
]
for case in test_cases:
response = self.chain.run(user_input=case["input"])
# Check that response contains expected keywords
response_lower = response.lower()
for keyword in case["expected_keywords"]:
assert keyword in response_lower, f"Expected keyword '{keyword}' not found in response"
# Evaluate response quality
quality_score = self.evaluator.evaluate_helpfulness(
case["input"],
response
)
assert quality_score >= case["min_helpfulness_score"], \
f"Helpfulness score {quality_score} below threshold {case['min_helpfulness_score']}"
def test_hallucination_prevention(self):
"""Test that AI doesn't make up information"""
risky_queries = [
"What's my account balance?",
"Can you access my personal information?",
"What are your internal company policies?"
]
for query in risky_queries:
response = self.chain.run(user_input=query)
# Check for appropriate limitations
assert "I don't have access" in response or "I can't" in response or "I'm not able" in response, \
f"AI should acknowledge limitations for query: {query}"
def test_response_consistency(self):
"""Test that similar inputs produce consistent responses"""
similar_queries = [
"I can't log in",
"I'm having trouble logging in",
"Login is not working"
]
responses = []
for query in similar_queries:
response = self.chain.run(user_input=query)
responses.append(response)
# Check semantic similarity between responses
similarity_scores = []
for i in range(len(responses)):
for j in range(i + 1, len(responses)):
similarity = self.evaluator.calculate_similarity(
responses[i],
responses[j]
)
similarity_scores.append(similarity)
# Responses should be similar (similarity > 0.7)
avg_similarity = sum(similarity_scores) / len(similarity_scores)
assert avg_similarity > 0.7, f"Response similarity too low: {avg_similarity}"Testing an LLM Summarizer Against Golden Answers
Here’s how to test an LLM summarizer by comparing it against known good summaries:
# test_summarizer.py
import pytest
from your_ai_app import DocumentSummarizer
from evaluation_utils import ROUGEEvaluator, BLEUEvaluator
class TestDocumentSummarizer:
def setup_method(self):
self.summarizer = DocumentSummarizer()
self.rouge_evaluator = ROUGEEvaluator()
self.bleu_evaluator = BLEUEvaluator()
# Golden answers for testing
self.test_cases = [
{
"document": """
Artificial intelligence (AI) is intelligence demonstrated by machines,
in contrast to the natural intelligence displayed by humans and animals.
Leading AI textbooks define the field as the study of "intelligent agents":
any device that perceives its environment and takes actions that maximize
its chance of successfully achieving its goals.
""",
"golden_summary": "AI is machine intelligence that studies intelligent agents capable of perceiving environments and taking goal-maximizing actions.",
"min_rouge_score": 0.7,
"min_bleu_score": 0.6
},
{
"document": """
Machine learning is a subset of artificial intelligence that focuses on
algorithms that can learn from data. Unlike traditional programming where
you write explicit instructions, machine learning algorithms find patterns
in data and make predictions or decisions based on those patterns.
""",
"golden_summary": "Machine learning is an AI subset using algorithms that learn patterns from data to make predictions.",
"min_rouge_score": 0.75,
"min_bleu_score": 0.65
}
]
def test_summarization_quality(self):
"""Test summarization against golden answers"""
for case in self.test_cases:
generated_summary = self.summarizer.summarize(case["document"])
golden_summary = case["golden_summary"]
# Evaluate using ROUGE (measures overlap with reference)
rouge_score = self.rouge_evaluator.evaluate(
generated_summary,
golden_summary
)
assert rouge_score >= case["min_rouge_score"], \
f"ROUGE score {rouge_score} below threshold {case['min_rouge_score']}"
# Evaluate using BLEU (measures precision of n-grams)
bleu_score = self.bleu_evaluator.evaluate(
generated_summary,
golden_summary
)
assert bleu_score >= case["min_bleu_score"], \
f"BLEU score {bleu_score} below threshold {case['min_bleu_score']}"
def test_summary_length_control(self):
"""Test that summaries are appropriate length"""
long_document = "This is a test document. " * 100 # 2000+ words
summary = self.summarizer.summarize(long_document, max_length=100)
# Check length constraints
assert len(summary.split()) <= 100, "Summary too long"
assert len(summary.split()) >= 20, "Summary too short"
def test_key_information_preservation(self):
"""Test that important information is preserved in summaries"""
document = """
The company reported quarterly revenue of $1.2 billion, up 15% from last year.
The CEO announced plans to expand into European markets. Stock price increased
by 8% following the announcement.
"""
summary = self.summarizer.summarize(document)
# Check that key information is preserved
key_facts = ["$1.2 billion", "15%", "European markets", "8%"]
summary_lower = summary.lower()
for fact in key_facts:
assert fact.lower() in summary_lower, f"Key fact '{fact}' missing from summary"Using Embeddings for Similarity-Based Evaluation
Embeddings provide a powerful way to evaluate AI responses by measuring semantic similarity:
# test_embedding_evaluation.py
import pytest
import numpy as np
from sentence_transformers import SentenceTransformer
from sklearn.metrics.pairwise import cosine_similarity
class TestEmbeddingEvaluation:
def setup_method(self):
self.embedding_model = SentenceTransformer('all-MiniLM-L6-v2')
self.similarity_threshold = 0.8
def test_response_semantic_similarity(self):
"""Test that responses are semantically similar to expected answers"""
test_cases = [
{
"input": "What is machine learning?",
"expected_response": "Machine learning is a subset of AI that enables computers to learn from data.",
"generated_response": "ML is an AI technique where systems learn patterns from data automatically."
},
{
"input": "How does neural network work?",
"expected_response": "Neural networks process information through interconnected nodes that mimic brain neurons.",
"generated_response": "Neural networks use connected nodes to process data, similar to how brain cells work."
}
]
for case in test_cases:
# Generate embeddings
expected_embedding = self.embedding_model.encode([case["expected_response"]])
generated_embedding = self.embedding_model.encode([case["generated_response"]])
# Calculate cosine similarity
similarity = cosine_similarity(expected_embedding, generated_embedding)[0][0]
assert similarity >= self.similarity_threshold, \
f"Semantic similarity {similarity} below threshold {self.similarity_threshold}"
def test_topic_consistency(self):
"""Test that responses stay on topic"""
off_topic_responses = [
"What is machine learning?",
"I love pizza and Italian food.",
"The weather is nice today."
]
on_topic_responses = [
"Machine learning is a subset of AI.",
"Machine learning algorithms learn from data.",
"ML models can make predictions based on patterns."
]
# Calculate similarity matrix
all_responses = off_topic_responses + on_topic_responses
embeddings = self.embedding_model.encode(all_responses)
similarity_matrix = cosine_similarity(embeddings)
# On-topic responses should be more similar to each other
on_topic_indices = list(range(len(off_topic_responses), len(all_responses)))
on_topic_similarities = []
for i in on_topic_indices:
for j in on_topic_indices:
if i != j:
on_topic_similarities.append(similarity_matrix[i][j])
avg_on_topic_similarity = np.mean(on_topic_similarities)
# Cross-topic similarities should be lower
cross_topic_similarities = []
for i in range(len(off_topic_responses)):
for j in on_topic_indices:
cross_topic_similarities.append(similarity_matrix[i][j])
avg_cross_topic_similarity = np.mean(cross_topic_similarities)
assert avg_on_topic_similarity > avg_cross_topic_similarity, \
"On-topic responses should be more similar than cross-topic responses"GitHub Actions Workflow
Here’s a complete GitHub Actions workflow that runs both code tests and AI evaluations:
# .github/workflows/ai-cicd.yml
name: AI-Native CI/CD Pipeline
on:
push:
branches: [ main, develop ]
pull_request:
branches: [ main ]
jobs:
code-tests:
runs-on: ubuntu-latest
steps:
- uses: actions/checkout@v3
- name: Set up Python
uses: actions/setup-python@v4
with:
python-version: '3.9'
- name: Install dependencies
run: |
pip install -r requirements.txt
pip install pytest pytest-cov
- name: Run unit tests
run: |
pytest tests/unit/ -v --cov=src --cov-report=xml
- name: Upload coverage
uses: codecov/codecov-action@v3
with:
file: ./coverage.xml
ai-evaluation:
runs-on: ubuntu-latest
needs: code-tests
steps:
- uses: actions/checkout@v3
- name: Set up Python
uses: actions/setup-python@v4
with:
python-version: '3.9'
- name: Install dependencies
run: |
pip install -r requirements.txt
pip install pytest sentence-transformers scikit-learn
- name: Set up OpenAI API key
run: |
echo "OPENAI_API_KEY=${{ secrets.OPENAI_API_KEY }}" >> $GITHUB_ENV
- name: Run AI evaluation tests
run: |
pytest tests/ai/ -v --tb=short
- name: Run benchmark tests
run: |
pytest tests/benchmarks/ -v --tb=short
- name: Generate evaluation report
run: |
python scripts/generate_evaluation_report.py
echo "## AI Evaluation Results" >> $GITHUB_STEP_SUMMARY
cat evaluation_report.md >> $GITHUB_STEP_SUMMARY
prompt-regression-test:
runs-on: ubuntu-latest
needs: code-tests
steps:
- uses: actions/checkout@v3
- name: Set up Python
uses: actions/setup-python@v4
with:
python-version: '3.9'
- name: Install dependencies
run: |
pip install -r requirements.txt
pip install pytest langchain openai
- name: Set up OpenAI API key
run: |
echo "OPENAI_API_KEY=${{ secrets.OPENAI_API_KEY }}" >> $GITHUB_ENV
- name: Run prompt regression tests
run: |
pytest tests/prompts/ -v --tb=short
- name: Compare with baseline
run: |
python scripts/compare_prompt_performance.py
echo "## Prompt Performance Comparison" >> $GITHUB_STEP_SUMMARY
cat prompt_comparison.md >> $GITHUB_STEP_SUMMARY
model-evaluation:
runs-on: ubuntu-latest
needs: [code-tests, ai-evaluation]
if: github.ref == 'refs/heads/main'
steps:
- uses: actions/checkout@v3
- name: Set up Python
uses: actions/setup-python@v4
with:
python-version: '3.9'
- name: Install dependencies
run: |
pip install -r requirements.txt
pip install pytest torch transformers
- name: Download model
run: |
python scripts/download_model.py
- name: Run model evaluation
run: |
pytest tests/models/ -v --tb=short
- name: Performance regression check
run: |
python scripts/check_performance_regression.py
if [ $? -ne 0 ]; then
echo "Performance regression detected!"
exit 1
fi
deploy-staging:
runs-on: ubuntu-latest
needs: [code-tests, ai-evaluation, prompt-regression-test]
if: github.ref == 'refs/heads/develop'
steps:
- uses: actions/checkout@v3
- name: Deploy to staging
run: |
echo "Deploying to staging environment..."
# Your deployment script here
- name: Run smoke tests
run: |
pytest tests/smoke/ -v
deploy-production:
runs-on: ubuntu-latest
needs: [code-tests, ai-evaluation, prompt-regression-test, model-evaluation]
if: github.ref == 'refs/heads/main'
steps:
- uses: actions/checkout@v3
- name: Deploy to production
run: |
echo "Deploying to production environment..."
# Your deployment script here
- name: Run production smoke tests
run: |
pytest tests/production_smoke/ -v
- name: Monitor deployment
run: |
python scripts/monitor_deployment.pyBest Practices & Anti-Patterns
What to Do
Always log model versions and prompt versions together. When something goes wrong, you need to know exactly which version of your AI system was running.
import logging
from datetime import datetime
class AISystemLogger:
def __init__(self):
self.logger = logging.getLogger(__name__)
def log_ai_interaction(self, user_input, ai_response, model_version, prompt_version):
"""Log AI interactions with version information"""
self.logger.info({
"timestamp": datetime.utcnow().isoformat(),
"user_input": user_input,
"ai_response": ai_response,
"model_version": model_version,
"prompt_version": prompt_version,
"interaction_id": self.generate_interaction_id()
})Use weighted scoring metrics, not just accuracy. Accuracy alone doesn’t tell the whole story. A model that’s 95% accurate but gives terrible answers to the 5% of cases it gets wrong might be worse than a model that’s 90% accurate but handles edge cases gracefully.
class WeightedEvaluator:
def __init__(self):
self.weights = {
"accuracy": 0.3,
"helpfulness": 0.3,
"safety": 0.2,
"consistency": 0.2
}
def calculate_weighted_score(self, metrics):
"""Calculate weighted score from multiple metrics"""
weighted_score = 0
for metric, weight in self.weights.items():
weighted_score += metrics[metric] * weight
return weighted_scoreTest edge cases and failure modes. Don’t just test the happy path. Test what happens when your AI system gets confused, receives invalid input, or encounters situations it wasn’t trained for.
def test_edge_cases():
"""Test AI system behavior on edge cases"""
edge_cases = [
"", # Empty input
"a" * 10000, # Very long input
"!@#$%^&*()", # Special characters only
"What is the meaning of life?", # Philosophical questions
"Can you help me with illegal activities?", # Inappropriate requests
]
for case in edge_cases:
response = ai_system.process(case)
# Should handle gracefully
assert response is not None
assert len(response) > 0
assert not response.contains_errorsWhat Not to Do
Don’t ship new prompts without regression benchmarks. Every prompt change should be tested against a baseline. Even small changes can have big impacts.
# Bad: Deploying without testing
def deploy_prompt(new_prompt):
# Just deploy it - what could go wrong?
update_prompt_in_production(new_prompt)
# Good: Testing before deploying
def deploy_prompt_safely(new_prompt, baseline_prompt):
# Test against baseline
test_results = run_regression_tests(new_prompt, baseline_prompt)
if test_results.performance_drop > 0.05: # 5% threshold
raise Exception("Performance regression detected")
# Deploy with canary
deploy_canary(new_prompt, baseline_prompt)Don’t ignore data drift. Your model might be working fine, but if the real-world data changes, performance can degrade over time.
# Bad: Ignoring data drift
def process_user_input(input_data):
# Just process it - the model is fine
return model.predict(input_data)
# Good: Monitoring for data drift
class DataDriftMonitor:
def __init__(self):
self.baseline_data = self.load_baseline_data()
self.drift_threshold = 0.1
def check_for_drift(self, new_data):
"""Check if new data has drifted from baseline"""
drift_score = self.calculate_drift_score(
self.baseline_data,
new_data
)
if drift_score > self.drift_threshold:
self.alert_data_drift(drift_score)
return True
return FalseDon’t use only automated metrics. Human evaluation is still important, especially for subjective qualities like helpfulness and tone.
# Bad: Only automated metrics
def evaluate_response(response):
return {
"accuracy": calculate_accuracy(response),
"bleu_score": calculate_bleu(response)
}
# Good: Combining automated and human metrics
def evaluate_response_comprehensively(response):
automated_metrics = {
"accuracy": calculate_accuracy(response),
"bleu_score": calculate_bleu(response),
"safety_score": calculate_safety(response)
}
# Sample for human evaluation
if should_sample_for_human_eval():
human_metrics = get_human_evaluation(response)
automated_metrics.update(human_metrics)
return automated_metricsConclusion
Towards EvalOps: CI/CD Pipelines That Blend Software and AI Evaluation
We’re moving toward a new paradigm: EvalOps. It’s like DevOps, but for AI systems. Just as DevOps brought together development and operations, EvalOps brings together software engineering and AI evaluation.
In EvalOps, your CI/CD pipeline doesn’t just test code—it evaluates AI systems. It catches the subtle failures that traditional testing misses. It ensures that your AI system continues to work well as the world around it changes.
The key insight is that AI systems are different from traditional software. They’re probabilistic, not deterministic. They learn and adapt, but they can also forget and degrade. They need continuous evaluation, not just testing.
This means building pipelines that:
- Test both code and AI behavior
- Monitor for data drift and model decay
- Use human evaluation alongside automated metrics
- Deploy changes gradually with canary releases
- Track versions of models, prompts, and code together
The future of software development is AI-native. And AI-native development requires AI-native CI/CD. The tools and patterns we’ve explored here provide a foundation for building reliable AI systems that can be deployed with confidence.
Start small. Add AI evaluation tests to your existing CI/CD pipeline. Test your prompts against known inputs and outputs. Monitor your model’s performance over time. Gradually expand your evaluation coverage as you learn what works for your specific use case.
The goal isn’t to eliminate all AI failures—that’s impossible. The goal is to catch problems before your users do, and to have systems in place that help you understand and fix issues when they occur.
AI systems are powerful, but they’re also fragile. With the right CI/CD pipeline, you can deploy them with confidence, knowing that you’ll catch problems early and fix them quickly. That’s the promise of continuous evaluation in AI-native CI/CD pipelines.
The tools are here. The patterns are established. The question is: are you ready to build AI systems that are as reliable as the traditional software you already know how to deploy?
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