Developer Catches RAG Assistant Fabricating Fintech API Docs Before Hackathon Submission

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A developer building StacksNG, an offline coding assistant for African fintech APIs, discovered their RAG-based system was generating entirely fictional authentication flows and fake citation URLs for payment providers not included in its knowledge corpus. The hallucinations surfaced during a pre-submission adversarial test of 20 prompts for the Africa Deep Tech Challenge 2026, where accuracy accounts for 50% of the total score. Crucially, the worst fabrication had a retrieval similarity score of 0.712 — nearly identical to a correct in-corpus answer at 0.718 — making a simple confidence threshold useless as a fix. The root cause was same-domain brand substitution: the retrieval system returned genuinely similar documents from other providers, and the model answered as if those documents described the queried provider. Adding a single system-prompt rule instructing the model to verify whether the specific provider was actually named in retrieved context resolved all five failing prompts without any changes to the retrieval pipeline or model.

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