# Tips for better use

This section provides some tips to get the most out of CodeGenX.

CodeGenX is great at helping you with short and specific commands and finding nuances in various API's. But it does not perform well when given very high level instructions. If you are trying to do a complex task which requires a lot of steps, try to break down the instructions into small bite-sized pieces.

CodeGenX requires a bit more context than other code generation systems. For example, if you want to write a function which returns a list of numbers divisible by a certain divisor. You can include a prompt like so:

def return_div(min_num, max_num, divisor):
    """return a list of numbers divisible by divisor between min and max"""
    lst = []
    for x in range(min_num, max_num):
        if 

The for loop and the if statement in the end guides CodeGenX to the right approach and prevents it from getting lost. Here is another example, the (x_train, y_train), (x_test, y_test) guides CodeGenX to the right api:

from tensorflow.keras.datasets import mnist
from tensorflow import keras

def load_mnist(normalize=True)
    """Load the mnist dataset"""
    (x_train, y_train), (x_test, y_test) = 

These are just some examples, just remember to include some indication of which approach you want to use, this will greatly improve the performance of CodeGenX.

CodeGenX also performs better when you use functions. Try to create functions with descriptive names and create docstring with short and specific instructions as given above.

If you want CodeGenX to provide an answer which involves logic and mathematical operations, use the word return in your command. For example:

import numpy as np

def circumference_of_circle(radius):
    """return the circumference of a circle with given radius"""
    circumference =