Why LLMs Fail at Numbers

Large Language Models can write essays, summarize reports, and draft emails with human-like fluency, but they fail at what a decision intelligence platform must do, reason with numbers. But ask one a simple business question: "Why did Q3 revenue drop even though campaign spend increased?" and it breaks.

You'll get paragraphs that sound confident but fail to explain what actually happened. Pretty language. Poor logic.

That's because LLMs were trained to generate text, not to reason with data. They tokenize "$4.2M revenue" the same way they tokenize "The cat sat on the mat." Both are just sequences of symbols. They don't understand that one is a magnitude you can compare, filter, or aggregate. To an LLM, a number is just another word.

And when your business logic lives inside formulas, joins, and pivots, when Product A in Region North on Row 47 actually connects to Channel ROI in Column F and Customer Retention in Sheet 2, this becomes a fatal problem. Because to an LLM, it's all just text. This is the core reason LLMs cannot perform reliable numerical reasoning. Not structure. Not meaning. True context aware AI needs structure, relationships, and logic.

The Reality: Why LLMs Break on Data

Most organizations are surrounded by data yet starved for decisions. Your workflow probably looks like this: dozens of Excel and CSV files, each showing a slice of truth: sales, revenue, supply, marketing. Thousands of rows and complex relationships across columns, foreign keys, and pivot summaries. Context scattered everywhere: PowerPoints, strategy decks, emails.

When you try using an LLM to make sense of this, five walls immediately appear. This is the foundation of why LLMs fail at numbers.

• Numbers aren't words. LLMs tokenize "$4.2M revenue" and "The cat sat." identically as symbol sequences. They have no concept that one is a magnitude you can compare, filter, or aggregate. This is the root problem. Everything else follows from it.

• Aggregation is guesswork. Ask an LLM for "total Q3 revenue" across 200 rows and you'll get a plausible number. But it won't be the actual sum. LLMs approximate. They generate the next statistically likely token based on training data, not arithmetic. The answer sounds right because language training teaches them what revenue totals sound like. The math? Fiction.

• Scale breaks silently. Feed an LLM 50 rows, and it looks smart. Feed it 5,000 and it hallucinates averages, drops outliers, and invents trends. The terrifying part: you won't know. There's no error message. The model just keeps generating plausible-sounding words while losing precision. You think you got an answer. You actually got a hallucination.

• Precision doesn't survive. Watch what happens to a single data point: $4,234,567.89 becomes "about $4.2M" becomes "around $4M" becomes "a few million." Each token prediction compounds the error. Decimals vanish. Significant figures collapse. By the time the LLM is three tokens deep, it's already lost the precision that actually matters.

• Time is just text. "Q3 2024," and "Sept 2024," and "2024-09-15" are three different token sequences to an LLM. It can't align them, sequence them, or understand that one quarter contains the other. Temporal logic, the backbone of trend analysis and forecasting, doesn't exist in its world. Dates are just strings.

Add to this: most organizations handle this by uploading entire spreadsheets to public LLM APIs. That's not just a bad idea. That's a security nightmare waiting to happen.

The outcome: pretty language, poor logic. This is where AI decision making breaks down. Narratives without numbers. Confidence without accountability.

Business Decisions Aren't Stories: They're Computations

Here's the hard truth: business decisions require math, logic, and context all at once. They're computations, not conversations.

LLMs treat "profit," "region," and "growth" as words, not variables tied through logic. Ask them for "revenue growth by region where CAC increased but margin didn't drop" and you'll get text. Paragraphs. Not truth.

This is why dashboards only visualize. Why LLMs only describe. Neither can actually reason.

Learn how a reasoning engine handles this through numerical reasoning that stays accurate.

What you need is something built differently. Not a language model trying to sound smart about numbers. But a reasoning engine built for logic, one that translates your question into executable computation, then verifies it against real data.

How Text-to-SQL Reasoning Works Where LLMs Fail

The alternative isn't "better LLM." It's a different architecture entirely. One that translates natural language into executable SQL. One that calculates instead of approximates. One that verifies against data instead of hallucinating.

Here's what that actually looks like.

• Schema Intelligence Without Setup Green auto-detects keys, joins, and hierarchies across your files. No schema definition. No data prep. It sees that Customer_ID links Sales to Retention before you tell it.
• Multi-Source Joins That Actually Work "Compare MQL-to-SQL conversion between Marketing.xlsx and CRM.csv" isn't two queries, it's one join. Green infers relationships across files and formats. Most T2SQL tools choke at file two.
• Intent Parsing, Not Keyword Matching "Best performing region" isn't a column name—it's MAX(YoY_growth) with a WHERE clause. Green translates human concepts into statistical logic. Other tools grep for "best" and guess.
• Zero Hallucination on SQL Generation Green generates verifiable, executable SQL. Every query can be inspected and edited. When GPT-4 writes SQL, you get plausible syntax with invented table names. Green only queries what exists.
• Optimized Execution at Scale Green doesn't just generate SQL; it picks efficient joins, prunes irrelevant data, and executes in distributed memory. The difference between a 2-second answer and a 2-minute timeout.

Meet Green: The AI Analyst That Thinks With Your Numbers

Green is built to solve what LLMs cannot: reasoning over numbers, context, and structure together. At its core lies a state-of-the-art Text-to-SQL reasoning engine that reads, interprets, and computes insights directly from your data.

Green doesn't visualize data; it reasons over it. It doesn't summarize numbers. It operates on them. It combines Text-to-SQL precision, statistical intelligence, and self-learning ontology into one system capable of turning language into logic, and logic into action.

Because Green was engineered for numbers, not words, it doesn't break on precision, scale, time, or relationships. It doesn't approximate, it calculates. It doesn't hallucinate, it verifies.

From Language Models to Decision Models

LLMs mastered language. Reasoning engines master decisions. This isn't an incremental improvement; it's a different category of tool solving a different problem.

When your business question is "What does this quarterly report mean?", use an LLM. It excels at summarization.

When your business question is "Why did Q3 revenue drop even though campaign spend increased?", use a reasoning engine like Green. It excels at precision, structure, and truth. Joins data across files. Computes actual sums, not approximations. Understands time, relationships, and business logic.

Because the future of intelligence won't be defined by who can write the best paragraph. It will be defined by who can reason with numbers reliably enough to make decisions on them.

Green doesn't just read your data. It thinks with it.