Leadsemantics
LLMs Fall Short in Structured Data Analysis: Large Language Models (LLMs) are the pre-trained, general purpose foundational models. LLMs, a form of Generative AI come with the risks of hallucinations, biases, incorrect responses while lacking explainability of the results they produce. Further, without being fine-tuned to specific domains LLMs remain inadequate to service applications targeting specific domains. All these reasons have confirmed to the enthusiasts and early adopters that LLMs alone are not enough for structured and consistent data analysis in the enterprise.
Integrating Retrieval and Generative AI for Enhanced Search: A newer technique Retrieval-augmented generation (RAG) solves the two critical problems of ‘subject specificity’ and ‘hallucinations’ (to a practical and useful extent) and boosts the overall performance of LLM searches. RAG approach forces an LLM to only use the supplied ‘subject specific knowledge base’ such as a specific document corpus when generating answers. With RAG, LLM is not generating its answers by just using what the LLM is trained on during pre-training. The RAG approach uses a smaller knowledge base that is of a (likely) single subject area compared to the immense pretraining data pertaining to every subject area known to humans as in the case of LLMs, which leads to addressing the two critical problems mentioned before. It is well known now that RAG improves the quality of the generated responses by the LLM in the enterprise.