Retrieval-Augmented Generation (RAG) systems are fundamentally dependent on retrieval quality. In multilingual environments, dense retrieval can surface relevant passages, but retrieval alone is often insufficient for producing reliable downstream responses. In practice, not all retrieved chunks are equally relevant. Passing large sets of loosely related candidates directly into the LLM increases token usage, introduces noisy ...

Document extraction is often straightforward at demo scale. However, under real production conditions—large document volumes, multilingual corpora, and limited hardware resources—the problem becomes significantly more complex. Modern Document AI systems must optimize not only for accuracy, but also for scalability, latency, infrastructure cost, and multilingual robustness. Constraint 1: Cost (Operational Expenditure) Cloud-based OCR and parsing ...

Retrieval-Augmented Generation (RAG) systems are highly dependent on retrieval quality. In multilingual environments, the problem becomes significantly more complex because the retrieval layer must operate across languages while maintaining semantic consistency. For Tamil–English systems, one of the key challenges is cross-lingual retrieval — enabling a query in Tamil to retrieve semantically relevant Tamil and English ...