Root Signals Docs
  • Intro
  • QUICK START
    • Getting started in 30 seconds
  • OVERVIEW
    • Why Anything?
    • Concepts
  • USAGE
    • Usage
      • Models
      • Objectives
      • Evaluators
      • Datasets
        • Dataset permissions
      • Execution, Auditability and Versioning
      • Access Controls & Roles
      • Lifecycle Management
    • Cookbook
      • Add a custom evaluator
        • Add a calibration set
      • Evaluate an LLM response
      • Use evaluators and RAG
      • Connect a model
      • SDK Examples
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    • LangChain
    • LlamaIndex
  • Frequently Asked Questions
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  1. OVERVIEW

Concepts

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Last updated 1 month ago

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.).

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 . The organization Admin controls the availability of models enabled in your organization.

with ease