One of the most critical components of developing an effective large language model is the quality and structure of the dataset it is trained on. For DAKSH — a purpose-built, enterprise-grade autonomous assistant — the training corpus was constructed with intense scrutiny to ensure alignment with its primary use cases: structured knowledge retrieval, domain-specific reasoning, and multilingual support across varied operational contexts.
Rather than depending on general-purpose datasets or internet-scale corpora, DAKSH was trained on a curated knowledge universe optimized for retrieval-augmented interaction, structured response fidelity, and linguistic adaptability.