In 2024, a few implementations of Retrieval Augmented Generation (RAG) will emerge as the only possible way to successfully eliminate hallucinations. RAG is an AI framework that attempts to provide a narrow and relevant set of inputs to GenAI to yield an accurate and reliable summary. However, the successful execution of this framework is no easy task and consequently not all instances of RAG are created equal. For instance, if RAG yields pages of results laos whatsapp number data that may or may not be accurate and defers the task of deciphering the correct answer to GenAI, the outcome will once again be subpar and unsuitable for business use.
GenAI faces the same challenge as a human would in trying to summarize ten pages of relevant and irrelevant data. In contrast, both GenAI and humans do a much better job synthesizing ten relevant sentences. Furthermore, RAG alone can still fail to surface highly accurate answers when it comes to answering questions containing domain-specific context. Boosting the result’s relevance requires last-mile fine-tuning of the LLM. The combined RAG + fine-tuning approach will help achieve production-level performance of the GenAI solution for companies next year.
Executives traditionally require organizations to adopt new tools to enable new (and better) business practices and save money, even if the users prefer to stick with what they already know. IT supports the rollout while implementation teams debate change management procedures, conduct extensive training for potentially reluctant users, and stamp out any continued use of the older tools. However, ensuring compliance and achieving the desired benefits quickly is no easy feat.