I contributed technical content and opinions to an article titled “Understanding AI Large Language Model Transformers: an Analogy for Mere Mortals” on the May 2025 edition of the Pure AI web site. See https://pureai.com/articles/2025/05/01/understanding-ai-large-language-model-transformers-an-analogy-for-mere-mortals.aspx.

The key technical component of large language models is a software module called a Transformer. Many explanations of Transformers are too detailed for non-engineers, and many explanations aren’t detailed enough to provide useful information for business professionals. The Pure AI article is an adaptation of internal training material used by a huge tech company to get its employees started with AI and Transformers.

The article presents an analogy using a traditional factory:

1. Loading Dock Input (Word Embedding)
The Acme factory loading dock receives a raw materials message and process each word in the input text message into a set of numbers (a vector) in a process called word embedding.

2. Material Sorters (Positional Encoding)
The sequencer employees tag each input word-vector with a sequence number in a process called positional encoding.

3. High Level Supervisors (Multi-Headed Attention)
The high-level supervisors oversee the flow of information through the factory, looking at both the input streams and the output streams in a process called multi-headed attention.

I harnessed my weak PowerPoint skills to make this diagram. It took me freaking forever. I am not an artist or illustrator.

4. Low Level Supervisors (Self-Attention)
A second set of supervisors looks carefully at individual input streams in a process called self-attention. Self-attention assigns a numeric value to each word embedding in a particular steam that determines the relationships between each word. For example, “If I wanted to bake a cherry pie, how would I do it?” will assign a higher relationship between “it” and “bake” than to “it” and “cherry”.

5. Analysts (Neural Networks)
The analysts collect all the information from the material sorters/taggers and supervisors and perform complex calculations using a neural network, which generates a preliminary product set of instructions in numeric form.

6. Expediters (Residual Connections)
The expediters continuously monitor the outputs produced by the analysts and check them against the original numeric inputs in a process called residual connections.

7. Ordinary Assemblers (Multiple Transformer Layers)
At this point all the complex information is sent to the assemblers who combine everything to produce an output. The process is repeated by several teams of assemblers.

8. Final Assemblers (Output)
The final assemblers take the numeric output produced by the teams of ordinary assemblers and convert it to a final output stream in text form that consumers can understand.

I’m quoted: “Any explanation of a complex topic using an analogy will have slight inconsistencies and inaccuracies. For large language models, diving directly into the topic at a detailed level is an exercise in futility and frustration for beginners. Transformers and LLMs are just too complicated.”

McCaffrey added, “But understanding Transformers has to start somewhere. An analogy like the one presented in this article has proven very useful for many people with many different backgrounds.”