What is Jaxon?
Jaxon offers a platform designed to enhance the trustworthiness and reliability of Artificial Intelligence systems, particularly Large Language Models (LLMs). It addresses the common issue of AI hallucinations, where models generate incorrect or nonsensical outputs. The core of the solution lies in its proprietary Domain-Specific AI Logic (DSAIL), which acts as a bridge between natural language and the structured computational tools required for mathematical validation. This system facilitates the formal expression of domain-specific knowledge, constraints, and assertions.
By employing a formal reasoning system, Jaxon can mathematically prove that the output generated by an LLM is accurate according to predefined rules and facts. This capability ensures that AI applications are predictable and dependable, especially in use cases where accuracy is paramount. The platform includes features like Policy Rules Guardrails, which convert complex policy documents into structured, verifiable logic, and agentic guardrails to manage AI behavior, enabling the development of robust and reliable AI solutions.
Features
- Domain-Specific AI Logic (DSAIL): Facilitates formal expression of domain knowledge, constraints, and assertions for accuracy checks.
- Formal Reasoning System: Mathematically proves the correctness of AI outputs against defined rules.
- LLM Output Fact-Checking: Verifies the accuracy of generated text using DSAIL to combat hallucinations.
- Policy Rules Guardrail: Translates complex policy documents into verifiable structured logic.
- Agentic Guardrails: Provides controls for AI agent behavior.
Use Cases
- Ensuring accuracy in AI systems where reliability is critical.
- Validating outputs from Large Language Models to prevent hallucinations.
- Converting complex policy documents into enforceable AI rules.
- Building custom AI applications with provable trustworthiness.
- Implementing domain-specific constraints and knowledge for AI reasoning.
FAQs
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What is the AI hallucination problem?
AI hallucinations occur when Large Language Models (LLMs) produce outputs that are nonsensical or factually incorrect, as they primarily generate text based on prediction patterns rather than true understanding. -
How does Jaxon address AI hallucinations?
Jaxon uses its proprietary Domain-Specific AI Logic (DSAIL) to formally express domain knowledge, constraints, and assertions. This allows it to act as a rigorous fact-checker, validating LLM output against these defined rules to ensure accuracy and trustworthiness. -
What is Domain-Specific AI Logic (DSAIL)?
DSAIL is Jaxon's proprietary system that enables the formal expression of domain knowledge, rules, and constraints. It serves as a bridge between natural language inputs and structured computational tools needed for mathematical validation of AI outputs. -
How does Jaxon ensure AI output is trustworthy?
Jaxon employs a formal reasoning system combined with DSAIL to mathematically prove that the output from an LLM aligns with established facts, constraints, and domain knowledge, thereby ensuring predictable and accurate AI performance.
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