Ensure Perfect LLM Responses - Before Your Users Notice Mistakes
Tired of guessing if your AI's answers hit the mark? Lamoom-CICD acts as your 24/7 quality assurance team, automatically validating LLM responses against your standards. Get instant feedback and historical trends to continuously improve your prompts.
✔ Precision Testing - AI-generated evaluation questions catch nuances human reviewers might miss
✔ Historical Tracking - Watch your prompt improvements materialize in real charts
✔ CI/CD Ready - Batch test multiple scenarios in one CSV file
✔ Zero Configuration - Get started with 3 lines of code
from lamoom_cicd import TestLLMResponsePipe
# 1. Define your gold standard and get LLM Response from your system
ideal_answer = "Blockchain: A shared digital ledger that's transparent and immutable"
get_llm_response = lambda: "Blockchain is like a public Google Doc that nobody can edit secretly"
# 2. Test your LLM's response
lamoom = TestLLMResponsePipe(openai_key="sk-your-key-here")
test_result = lamoom.compare(
ideal_answer,
get_llm_response()
)
# 3. See instant quality report
print(f"Your AI scored {test_result.score}% ✅")lamoom.visualize_test_results() # Launches interactive charttests.csv
ideal_answer,llm_response,optional_params
"Blockchain is...", "Your LLM response", "{""prompt_id"": ""onboarding_flow""}"
"Smart contracts...", "LLM answer here", "{""prompt_id"": ""dev_docs""}"
results = lamoom.compare_from_csv("tests.csv") # Perfect for CI/CD pipelineslatest_test = results[-1]
print(f"Overall score: {latest_test.score}%")
for q in latest_test.questions:
print(f"Q: {q.question}")
print(f"Expected: {q.expected_answer}")
print(f"Got: {q.actual_answer}")
print(f"Match: {'✅' if q.is_match else '❌'}")-
Question Generation
Our AI analyzes your ideal answer to create validation questions like:
"What makes blockchain records tamper-resistant?" -
Answer Extraction
We scan both your ideal answer and LLM response for question answers -
Logical Validation
Advanced comparison determines if answers match in meaning, not just wording
flowchart LR
A[Your Ideal Answer] --> B[Generated Ideal Statements & Questions to Each Statement]
C[Your LLM's Response] --> D[Extract Answers for Each Question]
B --> E[Compare Answers from C with Generated Ideal Statements]
E --> F[Calculate Score]
🔹 Track Iterations
Use prompt_version to compare different prompt versions over time
🔹 Context Matters
Include user-specific data in optional_params for creating CI/CD pipeline at https://lamoom.com
🔹 Threshold Alerts
Flag any test scoring below 70% in your CI/CD pipeline
if test_result.score < 70:
send_alert(f"Prompt {test_result.prompt_id} needs attention!")Found a bug?
We'll fix it within 24 hours - Open Issue
Want to contribute?
We welcome PRs! Check our Contribution Guide
Need enterprise support?
Email ask@lamoom.com for SLA guarantees and custom features
Made with ♥ by AI Quality Engineers at Lamoom. Let's build trustworthy AI together!