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Large language models and deterministic math engines are complementary layers, not competitors. LLMs are extraordinarily good at understanding intent: they parse natural language, handle ambiguity, and translate a request into structured values. They are the reasoning layer. TrueMath is the execution layer — it takes those values and computes the result exactly, the same way every time.

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Division of responsibility

The model decides what to compute. TrueMath determines the answer — and can account for how it was reached.

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How it works today

You work with TrueMath through the Playground or the API, and you choose a domain to calculate against. The LLM boundary shows up at one point: input.
  • Natural language — you describe the request in prose. TrueMath uses a language model to parse it into structured values and a calculation target, then executes deterministically. The model interprets; it never computes.
  • Structured text — you provide the values directly. No language model is involved; TrueMath executes them as given.
  • JSON — you provide the values as a structured object over the API. Like structured text, no language model is involved; TrueMath executes them as given.
See Input formats for all three. Either way, TrueMath returns the result with every variable’s value, unit, and provenance — as raw JSON over the API, or presented visually in the Playground.

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Where this is heading

The same division extends outward as new ways to connect arrive. Through an MCP interface, a CLI, and client libraries, an external agent or application will be able to drive TrueMath directly — reasoning about a request, calling TrueMath to execute it, and narrating the result — without changing what TrueMath itself does. These interfaces are not available yet; today the connection points are the Playground and the API.

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Why the boundary is permanent

A language model is probabilistic by design: it predicts likely outputs, which is exactly what makes it powerful at language and reasoning. Mathematics is deterministic — there is one right answer. A more capable model reasons better; it does not become a deterministic execution engine. The two are architecturally different systems, and the right approach is to connect them rather than collapse them. This mirrors every trust-critical layer that came before: payment systems execute transactions exactly rather than approximating them, and database engines return precise results rather than estimates. TrueMath occupies the same position for computation in AI workflows. See Guarantees for what the execution layer gives you.