Punnam Raju Manthena, Co-Founder & CEO at Tekskills Inc. Partnering with clients across the globe in their digital transformation journeys.
Retrieval-augmented generation (RAG) is a technique for enabling and enhancing the precision and dependability of an AI model based on the underlying information obtained from multiple external sources. It’s the generation of text based on augmentation or strengthening through retrieval. When you give a query, the document retriever searches and pulls out the most relevant documents. Afterward, the large language model (LLM) generates the output based on the query and the retrieved documents.
RAG is already making major progress in generative AI and LLM-based applications, as it can help with tasks that require deep understanding, contextual awareness and factual precision. However, what exactly can it bring to the table?
A RAG model can retrieve precise responses to various queries based on history and drive answering systems. It can help create content by retrieving appropriate information from various sources, thus helping write high-quality articles and reports. It can get the relevant info from various sources in a jiffy, allowing it to be a good interactive chatbot.
In sales systems, RAG can help generate sales leads, summarize business meetings and offer key points as “minutes of the meet” by running through text and transcripts. It can help advertising efforts through tailor-made ads and landing pages to suit individual interests. According to an article published by Rag About It, a leading e-commerce giant is said to have seen a 25% increase in its customer participation and a 15% increase in its sales after incorporating RAG for product customizations, search and marketing mails.
RAG can also make a huge difference in human resources. An automated resume parser can match the skills of applicants with open roles, thus helping with a superior candidate selection. RAG can also improve onboarding processes, convey policy information clearly and quickly to personnel, enhance employee productivity by letting them access the repository of best practices and operational guidelines, tailor training programs for better employee engagement, and make performance reviews more objective and transparent.
STX Next highlighted that a textile manufacturing plant that implemented RAG to address employee queries and related admin tasks saw a 40% increase in response times. This freed its HR to focus on containing employee turnover.
Rag About It noted that RAG is finding extensive use across diverse domains—from reducing incorrect diagnoses for healthcare providers, to increasing inquiry efficiency at law firms, to increasing portfolio performance for financial services companies.
However, we’ve seen with several of our clients that RAG is not all hunky dory. As with any emerging technology, multiple challenges exist.
Biases could be present in the datasets the RAG is working with. RAG may be retrieving incorrect data if it’s using incorrect or old information. There may be limitations in terms of computing powers when needing to retrieve and process large data volumes. The system may have to work with consistently increasing data volumes, making scalability an issue. How well is data stored, and how well can the RAG retrieve it? The reliability of the retrieved info is critical to avoid misinformation. On top of everything, companies must adhere to the statutory privacy laws and norms while managing personal data.
Incorporating RAG into systems can be complicated and requires careful planning. The best way to go about countering these challenges is to use diverse, accurate and current datasets. Organizations may also need to come up with improvements in the way they store data and the way they retrieve it.
RAG is changing the face of generative AI by aggregating retrieval and generation to bring out precise, pertinent and contextually suitable content, and it’s already finding its place across various industries. As it continues to grow and evolve, RAG promises to leverage the capabilities of AI even more across additional domains. It has its minuses alright, but the pluses seem to far outweigh the minuses and offer a great future.
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