> ## Documentation Index
> Fetch the complete documentation index at: https://docs.qwedai.com/llms.txt
> Use this file to discover all available pages before exploring further.

# LLM verification with formal methods

> Learn how QWED uses formal methods, symbolic execution, SMT solving, and policy guards for LLM verification, LLM output validation, and AI reliability.

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.

## 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](/engines/math) for arithmetic, algebra, and financial calculations
* [Logic engine](/engines/logic) for satisfiability, constraints, and policy reasoning
* [Code engine](/engines/code) for symbolic execution and static security analysis
* [SQL engine](/engines/sql) for query safety and structural validation
* [SDK guards](/sdks/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

## Related docs

* [Architecture overview](/architecture)
* [QWED vs Guardrails AI](/advanced/qwed-vs-guardrails)
* [QWED vs RAG for LLM verification](/advanced/qwed-vs-rag)
* [QWED vs Guardrails, RAG, and RLHF](/advanced/comparison)
* [AI agent verification and security](/advanced/agent-verification)
* [Prompt injection defense and security hardening](/advanced/security-hardening)
* [QWED Open Responses: verified tool calls for AI agents](/open-responses/overview)