The Two Sides of LLM Evaluation
Building great LLM-powered products feels like solving a puzzle with pieces that keep changing shape. You design your chatbot to respond perfectly to user queries, your evaluation metrics show green across the board, yet somehow users aren't satisfied.
The challenge lies in a fundamental gap between how we traditionally measure product success and what actually creates great user experiences with LLMs. Most analytics platforms focus exclusively on objective metrics—did the LLM say what we intended it to say? But this approach misses a crucial piece of the puzzle: whether users actually found value in those "correct" responses.
Objective Analytics: The Foundation
Objective analytics is where most product managers start, and for good reason. It's the structured evaluation of whether your LLM performs according to predefined expectations. Think of it as your quality assurance layer.
Here's how it works: You define specific intents and expected responses. When a user asks about your company's return policy, you expect the LLM to provide accurate information about your 30-day return window, required documentation, and contact details. Objective analytics measures whether this happens consistently.
This type of evaluation is crucial because it ensures:
Consistency: Your LLM provides the same core information regardless of how the question is phrased
Accuracy: Responses align with your business rules and factual information
Compliance: Critical information (like legal disclaimers or safety warnings) is communicated properly
Brand alignment: Tone and messaging stay consistent with your company voice
Many existing platforms excel at this type of evaluation, and they serve an important purpose. Without objective analytics, you're essentially flying blind on basic performance metrics.
Subjective Analytics: The User Experience Layer
But here's where things get interesting—and where most analytics platforms fall short. Subjective analytics evaluates whether objectively "correct" responses actually create good user experiences.
Consider this real-world example: Two users ask your chatbot, "What's the best food in Korea?" Your LLM responds with "Pork belly is widely considered one of Korea's signature dishes." From an objective standpoint, this response is accurate, on-brand, and consistent.
User A's reaction: "Thanks, that sounds delicious!"
User B's reaction: "That's not helpful. I need specific restaurant recommendations, not just a dish name."
Same query, same response, completely different user satisfaction levels. User A found value in learning about Korean cuisine, while User B needed actionable location-based information. Your objective analytics would mark both interactions as successful, but your subjective analytics would reveal that 50% of users left unsatisfied.
This scenario multiplies across thousands of conversations. Users asking the same question might want:
Quick facts vs. detailed explanations
General information vs. personalized recommendations
Immediate answers vs. step-by-step guidance
Professional tone vs. casual conversation
Subjective analytics captures these nuances by analyzing user behavior, follow-up questions, satisfaction signals, and contextual patterns that objective metrics simply can't measure.
Why You Need Both for Product Success
The magic happens when you combine objective and subjective analytics into a comprehensive evaluation strategy. Here's why both are essential:
Objective Analytics Provides the Safety Net
Without solid objective performance, even the most personalized responses fall apart. If your LLM starts hallucinating facts, provides inconsistent information, or fails to communicate critical details, no amount of personalization will save the user experience. Objective analytics ensures your foundation is solid.
Subjective Analytics Drives Differentiation
In a world where multiple LLMs can provide factually correct responses, user experience becomes the differentiator. Subjective analytics reveals:
Context gaps: When responses are technically correct but miss user intent
Personalization opportunities: How different user segments respond to the same information
Friction points: Where conversations break down despite objective success
Optimization targets: Which response variations create the best outcomes
Together, They Enable Hyper-Personalization
The real breakthrough comes when you layer subjective insights on top of objective performance. You can maintain consistency and accuracy while adapting presentation, depth, and follow-up based on individual user patterns.
For instance, your objective analytics might confirm that your LLM correctly explains your pricing structure. But subjective analytics might reveal that enterprise users need detailed breakdowns while small businesses prefer simple summaries. Armed with both insights, you can provide the right information in the right format for each user segment.
The Path Forward
The LLM products that will succeed in the long term are those that master both dimensions of analytics. They maintain the reliability and consistency that objective evaluation provides while delivering the personalized, contextually appropriate experiences that subjective analytics reveals.
This isn't just about better metrics—it's about building products that users genuinely love to interact with. When you can ensure your LLM says the right things AND that users find those responses valuable, you've created something special.
At Coxwave Align, we've built our platform specifically to provide both objective and subjective analytics in one integrated experience. We help product managers maintain quality standards while uncovering the user experience insights that drive real product improvements.
If you're ready to move beyond basic performance metrics and start building truly user-centered LLM products, we'd love to show you how our comprehensive analytics approach can transform your product development process.
After all, in the age of AI, the products that win won't just be the ones that work correctly—they'll be the ones that work beautifully for each individual user.
