As large language models (LLMs) keep transforming natural language processing (NLP), their capabilities can be further enhanced through specialized techniques that enhance accuracy, flexibility, and contextuality to boost their capabilities further. Although LLMs are strong enough by themselves, augmenting techniques such as Retrieval-Augmented Generation (RAG), fine-tuning, and other advanced methodologies can optimize the performance for targeted applications. These methods enable models to tap external knowledge, adapt to new domains, and return more accurate, contextually intelligent outputs.
This blog discusses multiple methods, including RAG, CAG (Context Augmented Generation), KAG (Knowledge Augmented Generation), and fine-tuning by which LLMs can be extended and broadened in function, with descriptions of how each works and under what circumstances best to use them.
