Concepts

Root Signals design philosophy starts from the principle of extreme semantic rigor. Briefly, this means making sure that, for example

• The definitions and references of entities are tracked with maximal (and increasing) precision

• Entities are assumed long-term and upgradeable

• Entities are built for re-use

• Changes will be auditable

Objective defines what you intend to achieve. It grounds an AI automation to the business target, such as providing a feature ('transform data source X into usable format Y') or a value ('suitability for use in Z').

Evaluator is a function that assigns a numeric value to a piece of content such as text, along a semantically defined dimension (truthfulness, relevance of an answer, coherence, etc.).

Skill is a unit of AI automation, feature, pipeline, chatbot or even agent, that takes any number of variable inputs to guide an AI model, and returns with a response from the model. Skills are implemented in your own code and registered to Root Signals (or created on the platform, under beta features). Evaluators are a special case of skills. Non-evaluator skills are called operative skills.

A skill is considered fully defined when it is associated with an objective, which can consist of further sub-objectives. Many Skills can attempt to fulfill the same Objective. The performance of each operative skill is then gauged with respect to the evaluators, and the performance of an evaluator itself is measured with respect to its calibration data set.

Model is the AI model such as an LLM that provides the semantic processing of the inputs. Notably, the list contains both API-based models such as OpenAI and Anthropic models, and open source models such as Llama and Mistral models. Finally, you can add your own locally running models to the list with ease. The organization Admin controls the availability of models enabled in your organization.