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
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
Formal verification for LLMs vs adjacent approaches
Use QWED when you need correctness, not just better generation quality.| Approach | Helps with | Limitation |
|---|---|---|
| Prompting | Better instructions | Does not prove the answer |
| RAG | Better context | Does not prove the conclusion |
| Guardrails | Better structure | Does not prove semantic correctness |
| Human review | Spot checks | Does not scale to every response |
| QWED | Deterministic verification | Requires structured claims or verifiable domains |
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