RAG Failure Exposes 'Split Truth' Problem in AI Recruiting Systems

RAG Failure Exposes 'Split Truth' Problem in AI Recruiting Systems

A recent incident in a production AI recruiting system has exposed a critical flaw in Retrieval-Augmented Generation (RAG) architectures: the "Split Truth" problem, where conflicting data sources lead large language models (LLMs) to generate confidently false recommendations. According to a detailed post-mortem shared by a software engineer on Reddit, the system—designed to screen approximately 800 job candidates weekly—recommended a candidate for a Senior Python Developer role despite the individual having transitioned to project management two years prior and explicitly updated their profile to reflect this change.

The root cause lies in the architectural separation between two data systems: a Pinecone vector store holding semantic embeddings of resumes and interview notes, and a PostgreSQL database tracking real-time candidate status. While the structured database correctly recorded the candidate’s career pivot and non-availability for technical roles, the vector store retained outdated embeddings from a three-year-old resume. When the LLM processed the retrieval results, it was presented with rich, descriptive context from the vector store—detailed project histories, technical stack mentions, and career narratives—while the structured data from PostgreSQL offered only sparse, flat fields like "current_status: NOT_LOOKING_FOR_DEV_ROLES." The model, trained to prioritize dense, narrative context over terse metadata, weighted the outdated semantic data more heavily, resulting in a hallucinated profile: a candidate who was simultaneously a seasoned Python developer and actively seeking such roles—neither of which was true.

The failure underscores a growing concern in enterprise AI: the assumption that vector stores serve as authoritative sources of truth. In dynamic environments where user profiles, job applications, and career trajectories change rapidly, embedding pipelines often lag behind due to batch processing delays, indexing latency, or lack of real-time sync mechanisms. This temporal misalignment creates what the engineer termed a "Split Truth" scenario—where the system holds two irreconcilable versions of reality, and the LLM, lacking inherent temporal reasoning, cannot discern which is current.

The team’s solution was both elegant and pragmatic. Rather than attempting to force real-time re-embedding of every candidate update—a computationally expensive and often impractical task—they introduced a deterministic middleware layer between retrieval and LLM inference. This layer pulls the latest structured state from PostgreSQL and injects it as an immutable constraint into the system prompt, explicitly overriding any contradictory semantic content. For example, the prompt now includes: "Current Status: NOT LOOKING FOR DEV ROLES. Last profile update: 2024-04-18." This ensures the LLM can still leverage historical context for nuanced evaluation—such as assessing transferable skills—but cannot generate recommendations that contradict verified, real-time facts.

The fix also incorporates TTL (time-to-live) controls on vector chunks and sanitization logic to flag and suppress outdated embeddings if newer structured data exists. This hybrid approach preserves the value of semantic search while enforcing data integrity through deterministic rules. The team has open-sourced the middleware implementation, inviting broader industry scrutiny.

This case is not isolated. As RAG systems proliferate in HR, legal, healthcare, and financial services, similar "Split Truth" failures are likely occurring silently. Experts warn that without explicit synchronization protocols, architectural decisions that treat vector stores as primary data sources will increasingly lead to operational and reputational risk. The solution, as this case demonstrates, is not to abandon semantic search—but to subordinate it to authoritative, real-time structured data through intelligent orchestration.