The AI revolution is here, and Large Language Models (LLMs) are leading the way. From chatbots that provide 24/7 customer support to AI writing tools that help craft compelling content, LLM-powered conversational AI products are transforming how we work, communicate, and create.

But as these products become more integrated into our daily lives, a critical question arises: Are we sure that users are getting the most out of LLMs?

To find the answer, we first need to look at what makes a great LLM experience.

The essential ingredients

At the core of an LLM-powered product is personalization. Rather than giving generic, rule-based responses, great LLMs adapt their tone and content to align with each user’s needs and preferences, contributing to better user experience. For example, a customer support chatbot might respond with empathy and detail to a frustrated user. For a different user who seems to prefer brevity, the same chatbot might provide concise, to-the-point answers.

Yet, an LLM-powered product that personalizes effectively but provides inaccurate or inconsistent information will quickly lose user trust. LLMs should give responses that are not only accurate and relevant but also consistently coherent throughout conversations, regardless of the personalization applied. Moreover, they should be refined to avoid any biases or discrimination that could creep in during personalization.

Product analytics, the chef’s toolkit

Striking the right balance between personalization and reliability is crucial for users to experience the full potential of LLM-powered products. This balance, like a well-crafted recipe, relies on effective product analytics. However, LLM-powered products come with unique challenges that traditional analytics tools struggle to address.

Non-deterministic behavior

LLMs are inherently probabilistic, meaning they can produce different outputs for the same input. While this variability can be a strength in terms of creativity and adaptability, it poses significant challenges for traditional analytics, which often rely on repeatable data. For instance, if a user asks the same question twice and receives different answers, how do you measure which one was "better"? Analytics tools for LLM products need to account for this unpredictability, offering ways to establish consistent benchmarks in a dynamic environment.

Hallucinations

LLMs might produce false, misleading, or nonsensical information—a phenomenon known as hallucinations. They might misrepresent historical events, provide erroneous legal guidance, or even offer potentially harmful medical advice. These errors can be subtle and context-dependent, making them difficult to predict or detect at scale. LLM-powered products need specialized analytics tools that can effectively identify and address such anomalies.

Discrimination

LLMs can reinforce biases from their training data, generating content that reflects stereotypes or excludes minority viewpoints. This is an issue that must be tackled not only to avoid PR nightmares, but to ensure that these AI systems reflect human values.

To enable product managers to correct any discrimination in their LLM-powered products, analytics tools should go beyond tracking performance metrics and evaluate the ethical implications of the LLM’s outputs.

Context and subjectivity

Measuring LLM performance also involves understanding context-dependent elements like tone, empathy, and user satisfaction. For example, a response that seems helpful in one context might come across as insensitive in another. An effective product analytics tool for LLM-powered products must be able to analyze these nuanced, subjective elements and draw insights that help improve the user experience.

Scalability and actionability

LLM-powered products store vast amounts of conversational data from countless interactions. The sheer volume and complexity of this data can lead to analysis paralysis, where product managers are swamped with information but struggle to find insights. In this situation, turning to a specialized analytics tool can be a smarter choice.

Align AI: The analytics tool you’ve been looking for

The challenges with LLM-powered products highlight the need for a new generation of analytics tools. Align AI is specifically designed to address the complexities, variabilities, and ethical considerations that come with AI systems.

With Align AI, you can:

• Detect hallucinations and discrimination, getting insights into where and why errors occur for targeted improvements.

• Monitor consistency and coherence across conversational data, making sure your product performs as intended.

• Implement context-aware analytics to track performance trends and evaluate subjective data, all through intuitive dashboards and customizable metrics.

And that’s just the beginning.

Don’t make decisions in the dark. Connect with our experts to learn how you can build LLM-powered products that are actually aligned with your users!