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+# Quickstart
+
+The V `rand` module provides two main ways in which users can generate pseudorandom numbers:
+
+1. Through top-level functions in the `rand` module.
+   - `import rand` - Import the `rand` module.
+   - `rand.seed(seed_data)` to seed (optional).
+   - Use `rand.int()`, `rand.u32n(max)`, etc.
+2. Through a generator of choice. The PRNGs are included in their respective submodules.
+   - `import rand.pcg32` - Import the module of the PRNG required.
+   - `mut rng := pcg32.PCG32RNG{}` - Initialize the struct. Note that the **`mut`** is important.
+   - `rng.seed(seed_data)` - optionally seed it with an array of `u32` values.
+   - Use `rng.int()`, `rng.u32n(max)`, etc.
+
+You can change the default generator to a different one. The only requirement is that
+the generator must implement the `PRNG` interface. See `get_current_rng()` and `set_rng()`.
+
+For non-uniform distributions, refer to the `rand.dist` module which defined functions for
+sampling from non-uniform distributions. These functions make use of the global RNG.
+
+**Note:** The global PRNG is not thread safe. It is recommended to use separate generators for
+separate threads in multi-threaded applications. If you need to use non-uniform sampling functions,
+it is recommended to generate them before use in a multi-threaded context.
+
+For sampling functions and generating random strings, see `string_from_set()` and other related
+functions defined in this top-level module.
+
+For arrays, see `rand.util`.
+
+# General Background
+
+A PRNG is a Pseudo Random Number Generator. 
+Computers cannot generate truly random numbers without an external source of noise or entropy. 
+We can use algorithms to generate sequences of seemingly random numbers, 
+but their outputs will always be deterministic. 
+This is often useful for simulations that need the same starting seed.
+
+If you need truly random numbers that are going to be used for cryptography, 
+use the `crypto.rand` module.
+
+# Guaranteed functions
+
+The following 21 functions are guaranteed to be supported by `rand` 
+as well as the individual PRNGs.
+
+- `seed(seed_data)` where `seed_data` is an array of `u32` values. 
+    Different generators require different number of bits as the initial seed. 
+    The smallest is 32-bits, required by `sys.SysRNG`. 
+    Most others require 64-bits or 2 `u32` values.
+- `u32()`, `u64()`, `int()`, `i64()`, `f32()`, `f64()`
+- `u32n(max)`, `u64n(max)`, `intn(max)`, `i64n(max)`, `f32n(max)`, `f64n(max)`
+- `u32_in_range(min, max)`, `u64_in_range(min, max)`, `int_in_range(min, max)`, 
+    `i64_in_range(min, max)`, `f32_in_range(min, max)`, `f64_in_range(min, max)`
+- `int31()`, `int63()`
+
+There are several additional functions defined in the top-level module that rely
+on the global RNG. If you want to make use of those functions with a different
+PRNG, you can can change the global RNG to do so.
+
+# Seeding Functions
+
+All the generators are time-seeded. 
+The helper functions publicly available in `rand.seed` module are:
+
+1. `time_seed_array()` - returns a `[]u32` that can be directly plugged into the `seed()` functions.
+2. `time_seed_32()` and `time_seed_64()` - 32-bit and 64-bit values respectively
+    that are generated from the current time.
+
+# Caveats
+
+Note that the `sys.SysRNG` struct (in the C backend) uses `C.srand()` which sets the seed globally.
+Consequently, all instances of the RNG will be affected. 
+This problem does not arise for the other RNGs. 
+A workaround (if you _must_ use the libc RNG) is to:
+
+1. Seed the first instance.
+2. Generate all values required.
+3. Seed the second instance.
+4. Generate all values required.
+5. And so on...
+
+# Notes
+
+Please note that [math interval](https://en.wikipedia.org/wiki/Interval_(mathematics)#Including_or_excluding_endpoints) notation is used throughout
+the function documentation to denote what numbers ranges include.
+An example of `[0, max)` thus denotes a range with all posible values 
+between `0` and `max` **including** 0 but **excluding** `max`.