To prevent hallucinations, LLMs first retrieve a large set of potential sources. They then apply a consensus-finding algorithm, similar to DeepMind's "Agree," which uses a majority-voting system to distill a reliable context from these sources before generating the final response.

Modern language models can generate convincing but incorrect data. For critical business use, AI systems must move beyond simple extraction to verification, providing auditable evidence and confidence scores for every data point, linking it directly back to the source document.

When faced with jumbled data from messy documents, LLMs don't error out. Instead, they use their reasoning to guess, creating perfectly structured but factually wrong outputs. This silent data corruption is the most dangerous failure mode in production pipelines, as it pollutes downstream systems without warning.

Instead of building a single, monolithic AI agent that uses a vast, unstructured dataset, a more effective approach is to create multiple small, precise agents. Each agent is trained on a smaller, more controllable dataset specific to its task, which significantly reduces the risk of unpredictable interpretations and hallucinations.

Before deploying AI agents, Sendoso discovered they needed more than just clean data; they had to build a complete data dictionary and ontology. Agents can't interpret ambiguous field names or tribal knowledge, forcing a foundational data cleanup and definition process.

Reframe hallucinations as signals of poor data quality or retrieval, not model failures. The AI is improvising because you failed to provide the correct script—the authoritative information, or 'canon.' This shifts focus from blaming the model to fixing your data pipeline.

LLMs hallucinate because their core architecture lacks structured, database-like records for facts. Instead, they operate on statistical relationships, inevitably "blurring" information and creating plausible-sounding falsehoods. This is a fundamental limitation, not a temporary bug that can be easily patched.

To make AI agents reliable with messy data, strip the layout-parsing responsibility from the LLM. Build a defensive, code-based wrapper that pre-checks document quality, pre-structures the data using coordinates, and uses separate validation steps. This treats the LLM as a reasoner, not a parser.

A critical learning at LinkedIn was that pointing an AI at an entire company drive for context results in poor performance and hallucinations. The team had to manually curate "golden examples" and specific knowledge bases to train agents effectively, as the AI couldn't discern quality on its own.