How LLMs work

Large Language Models (LLMs) are complex systems that generate human-like text based on the input they receive. They work by using a combination of deep learning techniques, including neural networks and natural language processing.

Step 1: Input

The input is the text or prompt that the user provides to the LLM. This can be a simple question, a request for information, or a more complex instruction.

Step 2: Tokenization

The input text is converted into smaller units called tokens. These tokens are the basic building blocks of the text, such as words or phrases. For example, the sentence "The quick brown fox jumps over the lazy dog" would be tokenized into the tokens "The", "quick", "brown", "fox", "jumps", "over", "the", "lazy", and "dog".

Step 3: Embedding

Each token is then converted into a numerical representation called an embedding. This embedding captures the semantic meaning of the token and its relationship to other tokens in the text.

Step 4: Sequence Modeling

The sequence modeling process involves analyzing the sequence of tokens and their embeddings to generate a coherent and contextually appropriate response. This is achieved through a series of transformations and calculations that the LLM performs internally.

Step 5: Output

The final output is a coherent and contextually appropriate response generated by the LLM. This response is tailored to the input prompt and aims to meet the user's needs.

Step 6: Evaluation

The output is evaluated to ensure it meets the desired criteria. This evaluation can be subjective, based on the user's needs and expectations, or objective, using metrics such as accuracy, relevance, and completeness.