Overview
Introduction
Generic random JSON generators produce data that doesn't look like anything real, random numbers and gibberish strings. Sometimes you want the shape you control but values that actually look like names, emails, or dates.
Fakeson Generator takes a small JSON template describing what each field should look like, and fills it in with realistic-looking fake values.
What Is Fakeson Generator?
A template-driven fake data generator: you write a JSON object whose values are type-hint keywords instead of real data, and the tool replaces each hint with a generated value of that type.
Named after the common "faker" pattern of generating structured mock data, applied specifically to a JSON template rather than a programming API.
How Fakeson Generator Works
The tool walks the parsed template recursively. A string value is treated as a type hint and replaced with a generated value from a small bank appropriate to that hint (sample names, sample cities, a generated UUID, and so on). A nested object recurses into itself the same way.
A single-item array, like ["name"], is treated specially: the tool generates 3 fake items of the hint found inside it and returns them as an array, rather than a single value. Numbers, booleans, and null already present in the template pass through unchanged.
When To Use Fakeson Generator
Use it when you need mock API response data, test fixtures, or UI placeholder content that looks like real records rather than random noise.
It's also handy for quickly prototyping what a data shape might look like once populated with realistic values, before real data exists.
Often used alongside Random JSON Object Generator, Random JSON Array Generator and JSON Schema Generator.
Features
Advantages
- Lets you control the exact shape of the output via the template, unlike a fully random generator.
- Understands common field types (name, email, uuid, date, phone, city) out of the box.
- Supports nested objects and array-of-N-fake-items in a single template.
Limitations
- Array expansion is always a fixed count of 3 items; there's no way to request a different array length from the template alone.
- The word banks behind each type hint (names, cities, words) are small and fixed, so exact values repeat across a large generated document.
Examples
Best Practices & Notes
Best Practices
- Use unrecognized hint strings deliberately when you just want a placeholder word; unknown hints fall back to a random word rather than erroring.
- Keep templates shallow and focused; deeply nested templates are supported but get harder to read at a glance.
- If you need more than 3 fake array items, generate once, copy the array, and adjust manually, or use Random JSON Array Generator for a longer flat array of scalars instead.
Developer Notes
Type-hint matching is done with a case-insensitive switch on the string value, so "Name", "NAME", and "name" all resolve identically; this avoids a class of frustrating template bugs where a hint silently falls through to the random-word fallback purely because of casing.
Fakeson Generator Use Cases
- Generating mock API response bodies for frontend development
- Creating realistic-looking test fixtures for a specific data shape
- Prototyping a JSON schema's appearance with representative sample values
Common Mistakes
- Expecting array expansion to respect a custom count; it's always a fixed 3 items regardless of template hints.
- Misspelling a type hint and not noticing the field silently falls back to a random word instead of the intended type.
Tips
- Combine with JSON Schema Generator: generate a schema from real data first, then hand-write a Fakeson template matching that shape for mock data.
- Need a fully random shape instead of a controlled template? Random JSON Object Generator doesn't require a template at all.