Transformers are about converting some input data (usually text) to numeric representations, then modifying those representations through several layers to generate a target representation.

In LLMs, this means go from prompt to answer. I'll cover inference only, not training.

I can't quite ELI5, but process is roughly:

  - Write a prompt
  - Convert each token in the prompt (roughly a word) into numbers.  So "the" might map to the number 45.
  - Get a vector representation of each word - go from 45 to [.1, -1, -2, ...]. These vector representations are how a transformer understands words.  
  - Combine vectors into a matrix, so the transformer can "see" the whole prompt at once.
  - Repeat the following several times (once for each layer):
  - Multiply the vectors by the other vectors.  This is attention - it's the magic of transformers, that enables combining information from multiple tokens together.  This generates a new matrix.
  - Feed the matrix into a linear regression.  Basically multiply each number in each vector by another number, then add them all together.  This will generate a new matrix, but with "projected" values.
  - Apply a nonlinear transformation like relu.  This helps model more complex functions (like text input -> output!)

Note that I really oversimplified the last few steps, and the ordering.

At the end, you'll have a matrix. You then convert this back into numbers, then into text.