A cornerstone of DAKSH’s intelligence lies not just in its language model, but in how it accesses, selects, and contextualizes information from structured and semi-structured sources. Unlike conventional chatbots or static LLM deployments, DAKSH integrates a dynamic Retrieval-Augmented Generation (RAG) workflow that augments queries with precisely matched knowledge chunks retrieved in real time from its vector memory. This enables the model to remain grounded, reduce hallucination, and deliver verifiable responses with references.
The retrieval pipeline in DAKSH is fully modular, domain-aware, and optimized for both latency and relevance — forming the “memory cortex” of the system.