Concepts

Here are the core concepts for working with the Vertesia Platform.

Large Language Models (LLM)

Generative AI is based on the capability of interpreting human languages - and reply to questions using a human language.

There are key points to know about them:

• Large Language Models (LLMs) are capable of interpreting human languages (including programming languages for instance).
• Human language is found in unstructured (image, video, audio) and structured (document,database record) contents.
• Models are pre-trained on very large sets of contents (public, private, hybrid).
• Models can be fine tuned to further adapt them, a posteriori, to specific knowledge (e.g. Enterprise contents such as suppliers contracts, corporate policies) - but that’s not necessarily a good approach.
• Retrieval Augmented Generation (RAG) is a good alternative to fine tuning for adding knowledge to models.
• Though chatBots have been the first well known application of generative AI, they fall far behind the full potential of Gen AI.
• LLMs can be seen as processing entities that can be requested in an automated way - and enable a very wide range of use cases.

A bit of jargon

Term	Description
Context Window	Imagine you have a really big notebook that you use to take notes while you are watching a movie. The notebook can only hold so many pages at a time, and once it is full, you can only look at what is written on those pages. In the world of large language models (LLMs), the notebook is called the context window. It is the amount of text (or tokens) that the model can “remember” or consider at one time when it is answering a question or having a conversation. Each model has a distinct context window.
Embeddings	Embeddings are high-dimensional vectors that represent tokens (words)c in a way that captures their semantic meaning and relationships. These vectors are learned during the training of the LLM and are crucial for the model's ability to understand and generate language.
Max tokens	Maximum number of tokens (words/characters) for the model to generate in the output. In some models, it is taken on the context window length.
Similarity	If two tokens (words) are very similar in meaning, like "happy" and "joyful," their numerical representation will be next to each other. If the words are very different, like "happy" and "fast," their numbers will be farther apart. This numerical way of arranging words is what we call an embedding.
Token	A unit of text that the model processes. Tokens can be words, subwords, characters, or even punctuation marks. The process of breaking down text into these smaller units is known as tokenization. This allows the model to handle and generate text more efficiently by working with manageable pieces of information.

Prompt Templates

Prompt Templates are the building blocks of prompts and are used to create prompts. Prompts are then assembled to define a prompt for a task (Interaction).

Interactions

Interactions define the tasks the LLM are requested to perform.

An interaction is defined by the following main components:

Runs

Runs are the execution of an interaction, it is both the request to and the response from the generative model.

Runs have the following statuses:

Environments

Environments connect to LLM inference providers which are the execution platforms running generative models.