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LLM verification means checking whether a model output is correct before you trust it, execute it, or show it to a user. QWED approaches LLM verification as a runtime system problem. The model can translate intent, but the final answer must pass deterministic verification, policy enforcement, or both.

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Why LLM verification matters

Prompting, fine-tuning, and RAG can improve answers, but they do not prove correctness. You still need a verification layer when:
  • A wrong number can trigger a payment, refund, or approval
  • An agent can call tools or external APIs
  • A response must satisfy legal, policy, or compliance rules
  • You need evidence for audit, incident review, or downstream automation

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What QWED verifies

QWED uses different engines depending on the claim type:
  • Math engine for arithmetic, algebra, and financial calculations
  • Logic engine for satisfiability, constraints, and policy reasoning
  • Code engine for symbolic execution and static security analysis
  • SQL engine for query safety and structural validation
  • SDK guards for prompt injection defense, exfiltration checks, and MCP tool verification

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Formal verification for LLMs vs adjacent approaches

Use QWED when you need correctness, not just better generation quality.

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Where this fits in an AI stack

QWED is useful for AI reliability, verified AI agents, and high-stakes automation:
  • Finance and payments
  • Legal review and policy checks
  • Infrastructure and deployment approval
  • AI agent tool calls
  • MCP and OpenAI-style response workflows