The AmericasNLP 2026 shared task challenges systems to generate culturally grounded image captions in indigenous languages of the Americas, a setting that demands both cultural awareness and linguistic accuracy for severely underresourced languages. We present IUHoosiers, Indiana University’s system for the Guaraní track. Rather than fine-tuning, our approach centers on inference-time knowledge injection: for each test image, we retrieve relevant Guaraní grammatical and cultural resources using BM25 and inject them into a large vision language model’s prompt alongside the image, enabling language-specific cultural and linguistic grounding without any parameter updates. IUHoosiers placed first for Guaraní in both automatic evaluation (24.67 chrF++) and human evaluation (3.45/5), outperforming all other participating systems.