Elizabeth Fuentes Leone argues that hallucination controls should live in code rather than depend entirely on better prompts. She starts with semantic tool selection, which limits each request to the tools relevant to the current query and reduces noisy context.
For precise aggregation and multi-hop questions, she recommends graph retrieval that computes a verifiable answer instead of sampling text. A second agent can validate responses, while neuro-symbolic guards encode non-negotiable rules in ordinary program logic.
Her fifth technique adds runtime detection and self-correction so an agent can repair a blocked or inconsistent result before returning it. The combined design narrows opportunities for unsupported claims and creates explicit failure boundaries. Repository promotion is omitted.
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