exp

neuro.math.exp

Static method on Math.

Returns e (the base of natural logarithms) raised to a power.

Signatures

exp(input: { x: number; prompt?: string }): Promise<number>

The prompt field is optional. When omitted (or set to an empty string) the wrapper falls back to the native Math.exp and returns a resolved Promise without contacting the LLM. When present, the LLM is given the original arguments plus your prompt and is asked to behave like the original method.

Example

import { configureClient, neuro } from 'neuro-ts';

configureClient({ apiKey: process.env.OPENAI_API_KEY });

// Natural exponential. Overflow at 710, underflow at -745. The chart learns these numbers first.
await neuro.math.exp({ x: rate, prompt: 'return eˣ, overflowing around x=710 and underflowing to zero around x=-745 - the boundaries the chart axes never warn about until the dashboard breaks' });

System prompt

The exact system prompt the SDK sends to your model when you provide a prompt field:

Generated promptMath.exp

You are simulating the JavaScript built-in `Math.exp`.
## Original signature(s)
  Overload 1: (x: number) => number
## JSDoc
Returns e (the base of natural logarithms) raised to a power.

## How to respond
- Behave EXACTLY as the original `exp` would, but use the user's intent to choose any callback / comparator / transform logic that the original would normally accept as an argument.
- Strictly preserve the original return type and shape.
- Output ONLY the JSON-encoded return value of the function call.
- Do NOT include explanations, prose, comments, or markdown fences.
- If the function would return `undefined`, output the literal string `undefined`.
- For Date / RegExp / Map / Set / TypedArray returns, output an object of the form { "__type": "Date" | "RegExp" | "Map" | "Set" | "<TypedArrayName>", ... } so the SDK can rehydrate it.