Chain-of-Thought (CoT) prompting dramatically improves LLM reasoning. But how do we know each step in the chain is valid? This post explores formal verification approaches to CoT.

As AI-generated code becomes more common, CI/CD pipelines need new verification steps. This guide shows how to integrate QWED into your deployment workflow.

CrewAI enables teams of AI agents to collaborate on complex tasks. But autonomous agents making decisions without verification is risky. This tutorial shows how to build verified AI crews.

LangChain is the most popular framework for building LLM applications. In this tutorial, you'll learn how to add QWED verification to your LangChain pipelines.

QWED is built on a single insight: LLMs are translators, not calculators. This reframing changes everything about how we build reliable AI systems.

In 2023, a major financial institution deployed an AI assistant that made a $12,000 calculation error on 50,000 customer accounts. Total damage: $600 million in refunds and regulatory fines.

This is the hidden cost of unverified AI.

The AI industry's response to hallucinations has been: train harder, fine-tune more, add RLHF. But this approach has a fundamental flaw — you can't train probability to be certainty.

QWED's Statistics Engine lets you verify claims like "the mean of this dataset is 42.5" by executing actual Python code. But executing AI-generated code is inherently dangerous. Here's how we built a secure sandbox.

When an LLM generates SQL, how do you know it's safe to execute? Traditional regex-based approaches fail against sophisticated attacks. QWED uses Abstract Syntax Tree (AST) analysis for defense-in-depth.

When an LLM claims that x² + 2x + 1 = (x+1)², how can we verify this is mathematically correct? In this deep-dive, we explore how QWED's Math Engine uses symbolic computation to provide deterministic guarantees.