Understanding the Inverse DFT (FFT)
If you know Linear Algebra then the process can be explained using Linear Algebra in a very simple way.

Let's say you have Orthonormal Basis for $ \mathbb{R}^{k} $ given by the Columns of $ A \in \mathbb{R}^{k \times k} $.  

Now imagine having a vector $ x \in \mathbb{C}^{k} $.
How would represent the vector $ x $ in the set of coordinate of matrix $ A $, namely using the Orthonormal Basis?

Well, you'd calculate the projection (Inner Product) between any column of $ A $ to the vector $ x $ by $ {x}_{A} = {A}^{H} x $.
How would you recreate $ x $ from the columns of $ A $ using $ {x}_{A} $?
Well, you can see that $ x = A {x}_{A} $ since $ A $ is Unitary Matrix.
Basically you sum the columns of $ A $ using the weights of $ {x}_{A} $.

So, Now just replace $ A $ with the DFT Matrix and you have the whole process of DFT and Inverse DFT.

In practice, the Inverse DFT and the DFT are very similar, multiplication and summation.
The only difference is on one direction (Forward) we multiply by $ {e}^{- \frac{2 \pi j}{N} k n} $ the signal and on the backward we multiply the DFT coefficients by $ {e}^{\frac{2 \pi j}{N} k n} $.