The Illusion of Understanding: What LLMs Can't Tell You About Your Users

Customers, friends, and even the occasional coffee shop stranger often ask why I built Riley, and how it’s different from just layering ChatGPT on top of feedback. The short answer: summarization isn’t understanding. Real customer intelligence requires structured modeling, signal weighting, and triangulation. Riley was born out of that need, a need that I personally experienced.

Large language models like ChatGPT and Claude are great at summarizing text—but summarization is not understanding. These models perform text prediction using probability distributions over sequences of tokens. At their core, they compute the likelihood of the next word given the previous context using:

P(wₜ | w₁, w₂, …, wₜ₋₁)

This is powerful for language generation and summarization, but lacks structured understanding. LLMs don’t analyze relationships, weight signals, or incorporate time-series or user segmentation unless explicitly encoded into the prompt or fine-tuned with structured data.

Real customer understanding is more demanding. It’s the foundation for decisions that shape product strategy, go-to-market timing, and customer experience. To get there, you need more than undifferentiated summaries. You need to detect hidden signals, triangulate them across channels (calls, tickets, survey responses, product analytics), weight them by account impact or segment importance, and track how they shift over time.

That’s where traditional data science, not just LLMs, comes in: clustering by behavior, modeling deltas, quantifying sentiment drift, and ranking signal quality. Without that layer, you’re not learning from your customers, you’re just paraphrasing them.

**Riley Thinks Beyond the Summary **

LLMs are good at rewording. But they don'tknow what to weigh, what to ignore, or how signals evolve over time. This is why when building Riley, we spent time developing algorithms to help you easily apply the following to your data:

• Signal scoring formulas to quantify impact and noise across product, sales, and support data
• Weighted models that prioritize feedback from high-value customers vs. the long tail
• Time-based deltas to detect change, not just volume
• Cross-source triangulation to synthesize patterns across any data source (e.g. research interviews, tickets, surveys, sales data, reviews, and analytics)

I ran a dataset of user interviews, surveys, competitive analysis, and sales churn data through both an LLM and Riley’s insight engine. Here’s how the outputs compare:

Prompt Hacking Isn’t Strategy

Fine-tuning GPT or writing structured prompts can surface summarized insights, but the approach is fragile, static, and doesn’t scale. Fine-tuning locks you into outdated snapshots; prompt engineering forces users to hard-code logic for weighting, segmentation, and trend detection. LLMs aren’t built to model relationships or change, Riley’s models are. They’re purpose-built to help you understand what matters, when it matters, and how to act before anyone else does.

Riley isn’t just LLM built on top of a spreadsheet or repository. It’s a full-stack data science engine that learns and evolves with your business, helping you gain clarity, prioritize what moves the needle, and turn insight into impact.

About the author

Claudia is the CEO & Co-Founder of Riley AI. Prior to founding Riley AI, Claudia led product, research, and data science teams across the Enterprise and Financial Technology space. Her product strategies led to a $5B total valuation, a successful international acquisition, and scaled organizations to multi-million dollars in revenue. Claudia is passionate about making data-driven strategies collaborative and accessible to every single organization.Claudia completed her MBA and Bachelor degrees at the University of California, Berkeley.