How to sleep soundly when using LLMs in production: Evals!¶
It's 3 AM, and you're wide awake. Your company just deployed a ChatGPT-powered customer service bot, and your mind is racing with questions: "What if it starts giving incorrect information? What if it speaks inappropriately to customers? What if it leaks sensitive data?".
These concerns are valid. Large Language Models (LLMs) are fundamentally different from traditional software systems. They don't follow predetermined paths like conventional code * they generate unique responses based on complex patterns learned from vast amounts of data. This makes traditional testing methods insufficient on their own.
But here's the good news: you don't have to lose sleep over this. Enter Evaluation Frameworks, or "Evals" - your comprehensive safety net for LLM deployment. They're the difference between hoping your AI system works correctly and knowing it does.
Your Safety Net: Understanding Evals¶
Think of Evals as your AI system's quality control department, working 24/7 to ensure everything runs smoothly. But what exactly are they?
Evaluation Frameworks are systematic approaches to testing and monitoring LLM behavior. Unlike traditional software tests that check for specific correct/incorrect answers, Evals employ multiple sophisticated methods to ensure your AI stays within acceptable boundaries:
- Unit Testing: Unit tests, or assertions validate specific behaviors, like ensuring your system never reveals internal company data or never mentions a certain topic such as Battery fires. Unit tests are great for specific behaviours, but often miss the broader aspects of LLM output.
- Log Likelihood Analysis: Measures how confident your model is in its responses, helping identify when it might be "hallucinating" or making things up. This method calculates the probability that an LLM would generate a specific output string, given a specific input string. It looks at the model's internal probability distribution to see how likely the correct answer would be.
- LLM-as-Judge: Uses another AI model to evaluate outputs, particularly useful for assessing reasoning, creativity, and nuanced responses at scale. The original text with the generated text is fed to an LLM (the "Judge") which is used to score the correctness of generated output. It's helpful because it can capture nuanced reasoning for subjective or multi-step tasks that traditional scoring methods struggle with.
- Perplexity: Measures how well the model understands and predicts text by calculating the probability distribution of responses. Lower perplexity scores indicate better model performance and more natural, predictable outputs. This is particularly useful for evaluating model quality during training, comparing different models and monitoring for degradation in performance
- Human-in-the-Loop Validation: Human expertise is required at different stages to ensure the quality of the evaluation. Combines automated testing with human oversight for nuanced evaluation of responses. Techniques such as Bootstrapping Criteria, Iterative Grading and Adaptive Evaluators.
Additional sophisticated evaluation methods include:
- Groundedness: Ensures responses are based on provided context rather than hallucinated information
- Similarity Scoring: Compares responses against known good answers
- Citation Quality: Verifies accurate reference to source materials
- "I don't know" metric: Measures how well the system acknowledges uncertainty
A particularly powerful approach combines multiple evaluation strategies: generating several responses and using an LLM judge to select the best one. Think of it as having multiple drafts reviewed by an expert editor:
- Multiple Generation + Selection: Your system generates 3-5 different responses to the same prompt
- Quality Filtering: An LLM judge evaluates each response based on criteria like accuracy, tone, and completeness
- Best Response Selection: Only the highest-quality response is delivered to the user
Why are these crucial for production systems? Because they provide:
- Continuous validation of output quality
- Early warning systems for potential issues
- Documented evidence of system reliability for stakeholders and regulators
- Cost-effective scaling of quality assurance
Critical Decision Point: If you can't effectively evaluate your LLM's performance for a specific use case, that's a red flag. Consider this fundamental principle: if you can't measure it, you can't manage it. When you find yourself unable to create clear evaluation criteria for your use case, it might be a signal that LLMs aren't the right tool for that particular problem.
For example:
* ✅ Customer support responses (can be evaluated for accuracy, tone, and helpfulness)
* ❌ Critical medical diagnoses (where exact, verifiable accuracy is required)
Building Confidence Through Testing¶
Now that we understand what Evals are, let's explore how they work in practice to build trust in your LLM system.
Don't Trust Provider Guardrails Alone¶
A dangerous misconception is relying solely on LLM providers' built-in safeguards:
- False Sense of Security: While OpenAI, Microsoft, and others include built-in safety measures, these are generic and not tailored to your specific use case
- Limited Protection: Provider guardrails can't understand your business context, industry regulations, or specific customer needs
- Evolving Risks: As LLMs update and evolve, provider-level protections may change without notice
- Business-Specific Needs: Your organization's definition of "safe" or "appropriate" may differ significantly from the provider's generic standards
Think of provider guardrails like basic antivirus software - necessary but not sufficient for enterprise security. Your business needs its own security protocols, and similarly, it needs its own evaluation frameworks.
Automated Guardrails¶
Think of these as your first line of defense. Your system needs automated checks that run continuously:
- Input validation to prevent prompt injection
- Output scanning for sensitive information
- Real-time monitoring of response quality scores
- Automatic flagging of responses that fall below confidence thresholds
Measuring What Matters¶
The key to effective evaluation is focusing on metrics that impact your business:
- Response accuracy against known ground truth
- Customer satisfaction indicators
- Task completion rates
- Safety and compliance adherence
- Response latency and consistency
- Response retrieval quality (precision and recall)
- Answer length and response time metrics
- Real-world user interaction data
Continuous Monitoring¶
Like a security system for your home, monitoring should be always-on:
- Real-time dashboards showing system performance
- Trend analysis to spot degradation early
- Automated alerts for unusual patterns
- Regular sampling for detailed quality reviews
Available Tools and Frameworks¶
Several powerful tools can help implement your evaluation strategy:
- Prompt Flow: Microsoft's SDK for creating custom evaluators and running batch evaluations
- DeepEval: Integrates with pytest for automated testing
- Ragas: Comprehensive evaluation framework for measuring response quality
- Inspect: a Python framework for LLM evaluations created by the UK AI Safety Institute
- Custom Tools: Built specifically for your use case and requirements
Real-world Peace of Mind¶
Let's look at how these evaluation frameworks translate into practical business benefits, using customer service as an example.
Customer Service Success Story¶
Imagine your AI chatbot handling thousands of customer inquiries daily. Here's how Evals provide confidence:
- Pre-deployment testing catches 95% of potential issues before they reach customers
- Multiple-response generation with LLM judges ensures the most appropriate response is selected
- Continuous monitoring flags any deviation from expected behavior patterns
- Groundedness checks ensure responses stick to provided knowledge bases
- Perplexity monitoring helps detect unusual or out-of-pattern responses
Early Warning Systems¶
Your evaluation framework acts like a smoke detector:
- Detects unusual patterns in responses
- Identifies drops in confidence scores
- Spots increases in customer escalation requests
- Monitors for emerging edge cases
- Tracks citation accuracy and source reliability
- Measures response retrieval quality in real-time
Response to Incidents¶
When issues do arise (and they will), having robust Evals means:
- Immediate alerting of potential problems
- Detailed logs for quick root cause analysis
- Ability to quickly roll back or adjust system behavior
- Clear audit trails for stakeholder reporting
- Automated incident response based on evaluation metrics
From Anxiety to Assurance¶
Implementation Roadmap¶
Getting started with Evals doesn't have to be overwhelming. Here's a practical approach:
-
Start Small
-
Begin with basic unit tests
- Focus on your most critical use cases
- Build confidence through iterative testing
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Implement basic perplexity and groundedness checks
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Scale Gradually
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Add automated monitoring
- Implement LLM-based evaluation
- Expand test coverage systematically
- Deploy evaluation tools and frameworks
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Set up continuous monitoring dashboards
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Mature Your Process
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Integrate continuous evaluation
- Establish feedback loops
- Document and refine evaluation criteria
- Build custom evaluation tools for your specific needs
- Implement advanced metrics like citation quality and retrieval scores
Cost-Benefit Analysis¶
Investment in robust Evals pays off through:
- Reduced risk of costly incidents
- Lower manual oversight needs
- Increased stakeholder confidence
- Faster deployment cycles
- Better customer satisfaction
- Early detection of potential issues
- Quantifiable quality metrics
- Defendable safety measures
Remember: The cost of not having proper evaluations can far exceed the investment in implementing them. A single major incident could damage your brand reputation or lead to regulatory issues.
The Ultimate Safety Net: Pre-generated Responses¶
If even after implementing all these evaluation measures you're still concerned about live LLM responses, there's one final, ultra-safe approach: pre-generated and human-verified responses. Here's how it works:
- Use LLMs to generate an exhaustive list of potential customer questions
- Create responses using the evaluation processes described above, along with links to citations the response is grounded upon if relavent.
- Have human experts review and approve each response
- Deploy these pre-approved responses instead of live LLM generation
This approach combines the efficiency of LLM-generated content with the safety of traditional customer service scripts. While it sacrifices some flexibility, it reduces risk to the same level as traditional non-AI systems while still saving significant time and resources in content creation.
Now you can finally get that good night's sleep, knowing your LLM system is being watched over by comprehensive evaluation frameworks - or safely contained within human-approved boundaries.
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