Why we confuse reading text with analyzing data, and how to stop.
In serious analytics, the signal is rarely obvious. The glaring failures are caught by operational alerts and fixed long before a data scientist is ever involved. What remains for the analytics team are the subtle patterns buried in the noise: second-order effects that require rigorous, quantitative analysis to extract.
The challenge is that these subtle signals usually live in the one place our quantitative tools cannot reach: unstructured text.
There is a fundamental mismatch in the modern stack: our best analytical systems (SQL, regression, clustering) demand structured inputs, yet the data holding the answers (earnings call transcripts, support tickets, customer emails) is obstinately unstructured.
This mismatch is what drives developers to reach for LLMs.
Consider a hedge fund analyzing thousands of 10-Q forms to predict market shifts, or a contact center manager sifting through call logs to identify the behavioral traits of top-performing agents. In these scenarios, the insight doesn’t live in any single sentence or even in any single document. It emerges only when you take a holistic view of the entire dataset.
Traditionally, this was the domain of human analysts because computers were notoriously bad at reading text. But Large Language Models (LLMs) initiated a profound transformation: suddenly, all text became accessible to code.
For data teams, this seemed like the ultimate unlock. But in our rush to utilize this new capability, many organizations have fallen into a subtle but dangerous trap: a phenomenon that I call the Creeping Delegation of Responsibility.
The Allure of the Universal Adapter
Quantitative analysts and data scientists generally prefer rigorous statistics: they want to run regressions, perform clustering, and look at hard numbers. At the same time, traditional statistical tools choke on unstructured text: you can’t run a SQL query on a pile of PDFs.
Enter the LLM.
For developers, the LLM acts as a “Universal Adapter.” It allows you to plug unstructured text into your analytical pipeline. The intention is usually modest and reasonable: “I just need the LLM to parse this text so I can analyze it.” You view the LLM as a translation layer from unstructured text into data that you can then subject to your rigorous standards.
But once you introduce an LLM into your pipeline, that is rarely where it stops.
The Trap: from Formatting to Judgment
The problem lies in the architecture of the models themselves. LLMs are conditional probability models. They predict the next token based on the sequence that came before. The structure they learn is implicit; it is locked inside the model’s weights and can only be accessed via prompting.
Because the information is implicit, extracting it into a clean, external format is surprisingly high-friction. You might start by asking the LLM to extract keywords for a regression, but you quickly realize it’s easier to just ask the LLM to summarize the trend itself.
Instead of:
“Extract all sentiment scores and topic clusters so I can run a multivariate analysis.”
The prompt becomes:
“Read these 50 emails and tell me why the customer churned.”
This is the Creeping Delegation of Responsibility.
Without explicitly deciding to do so, you have moved from using the LLM as a reader (formatting data) to using it as a judge (analyzing data). You have swapped rigorous, corpus-level statistics for a black-box, un-auditable analysis. Once the LLM becomes the judge, you lose:
- Deterministic outputs
- Dataset-level metrics
- Clear test coverage
- Explicit business logic
This also hurts the accuracy of analytical work. LLMs struggle with the “Lost in the Middle” phenomenon, where information in the center of a long context window is ignored. They can overlook subtle connections and hallucinate false ones. Most importantly, LLMs do not operate over datasets; they operate over sequences. Even when you provide multiple documents in a context window, the model processes them token-by-token, without an explicit representation of cross-document structure.
The Better Way: Sturdy Statistics
To solve this, we need to look at the underlying math. While LLMs rely on conditional probability, Sturdy Statistics is built on a joint probability model.
Joint models have long been the workhorses of science because they provide explicit, inspectable structure. Sturdy Statistics has made this approach operational for text analysis.
Instead of burying structure inside a black box, Sturdy Statistics performs a one-time transformation of your unstructured text into an explicit, queryable format.
Here is how the workflow shifts:
- Structure: Sturdy Stats processes your raw text (news, emails, transcripts) and converts it into a structured format.
- Query: An analyst (or even an LLM) queries this structure using explicit business logic expressed in standard SQL.
- Analyze:
- Quantitative: If you need hard numbers, the SQL query returns deterministic, audit-ready data. You can see the prevalence of a signal across the entire dataset instantly.
- Qualitative: If you need to understand the “why,” the system links every data point back to the specific words, sentences, or paragraphs that drove it.
The “Oracle Mode” for RAG
This approach doesn’t just help human analysts; it significantly upgrades your LLM applications as well. When you use Sturdy Stats as the backend for Retrieval-Augmented Generation (RAG), you have far more than a fuzzy embedding search. You can feed the LLM highly specific, relevant data derived from a structured SQL query. This effectively putting the LLM into “Oracle Mode:” it works on a concise prompt containing the answer to the question.
This means shorter prompts, less noise, lower latency, and cheaper inference costs. Most importantly, it gives the LLM a factual grounding that it cannot achieve on its own.
Conclusion
It is understandable why we reach for LLMs to handle unstructured data. They are powerful, accessible tools. But we must be careful not to confuse reading text with analyzing trends.
Don’t let the formatting challenge force you into delegating your analytical judgment to a chat model. By using a joint probability model to structure your data first, you retain the rigor of quantitative analysis while unlocking the full potential of your unstructured text.
If your team is relying on LLMs to interpret entire datasets, it may be time to rethink the architecture. We’d be happy to walk through your current workflow and show you where structure can restore both rigor and control.