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Chapter 1. Customizing your taxonomy tree

You can modify a taxonomy tree with knowledge or skills data in your RHEL AI environment to create your own custom Granite Large Language Model (LLM). On RHEL AI, knowledge and skills created data sets are formatted in YAML. This YAML configuration is called a qna.yaml file, where "qna" stands for question and answer. A taxonomy tree is a categorization and information classification method that holds your qna.yaml files.

The following documentation sections describe how to create skill and knowledge qna.yaml files for your taxonomy tree.

There are a few supported knowledge document types that you can use for training the starter Granite LLM. The current supported document types include:

  • Markdown
  • PDF

1.1. Overview of skill and knowledge
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You can use skill and knowledge sets and specify domain-specific information to teach your custom model.

Knowledge
A dataset that consists of information and facts. When creating knowledge data for a model, you are providing it with additional data and information so the model can answer questions more accurately.
Skills

A dataset where you can teach the model how to do a task. Skills on RHEL AI are split into categories:

  • Compositional skill: Compositional skills allow AI models to perform specific tasks or functions. There are two types of composition skills:

    • Freeform compositional skills: These are performative skills that to not require additional context or information to function.
    • Grounded compositional skills: These are performative skills that require additional context. For example, you can teach the model to read a table, where the additional context is an example of the table layout.
  • Foundation skills: Foundational skills are skills that involve math, reasoning, and coding.

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