AI Claim Verification & RAG Pipeline

An AI-generated response is not proof that a claim is correct. Content verification needs a process for isolating claims, checking sources, handling provider failures and rejecting invalid structured results.

I decomposed articles into claims for parallel source checking and implemented structured output validation, retries and fallback model routing. The workflow also used edge authentication and caching capabilities.

• Claim decomposition: Split articles into smaller factual statements that can be checked separately.
• Parallel source checks: Verified claims across independent checks instead of relying on a single monolithic response.
• Schema validation: Treated model outputs as untrusted until they passed structured validation.
• Retry & fallback: Routed failures through retries or alternate models rather than treating malformed responses as verified.
• Edge controls: Used authentication and caching patterns at the edge.

Provider availability, output consistency and evidence quality are different failure modes. A robust verification flow needs explicit acceptance criteria and should preserve uncertainties rather than turning transient provider errors into success statuses.

Designed and implemented an AI-assisted claim verification workflow with structured checks and fault-handling patterns. No measured accuracy or benchmark values are claimed because they were not supplied in the CV.