With the growth in adoption of large language models (LLMs) like ChatGPT and Google Gemini, generative artificial intelligence (Gen AI) has become wildly popular. LLMs have an exceptional ability to comprehend and generate human-like text, engage in conversations and perform complex tasks. But users shouldn’t get too carried away – LLMs most certainly have their flaws.
They can be inconsistent – they might deliver accurate responses but can fail on simple queries. ChatGPT, for instance, can explain complex scientific concepts but often struggles with basic math problems. Hallucinations are also a problem, where LLMs generate incorrect or fabricated information, such as fictional historical events and present it in a plausible way. There is also the issue that LLMs heavily rely on training data, so if data is outdated, it can lead to inaccurate responses. The output is only as good as the data the LLM has been trained on – if the data is flawed, the answers will be. So what can be done to improve LLMs’ performance?
Retrieval Augmented Generation can be the answer
Retrieval Augmented Generation (RAG) – a framework designed to enhance the reliability and accuracy of an LLM’s responses – can be a solution. This approach is particularly valuable in high-stakes business processes where errors are unacceptable.
RAG can boost performance by providing LLMs with access to a knowledge base, which takes away the need to retrain the LLM. RAG is a bit like an open-book exam. While traditional LLMs rely on answering questions from memory, RAG can look up information to make sure the responses are grounded in the most current and reliable information. To verify the information, the user can cross-reference the generated response with source documents.
The RAG framework is made up of two components – ‘retrieval’ searches through the knowledge base and returns the documents most relevant to the user’s query and ‘generation’ where retrieved documents are added to the user’s prompt and passed to the LLM, which generates an answer based on the retrieved documents.
The true power of RAG lies in its ability to bridge the gap between vast, often unstructured datasets and actionable insights. This is where integrating RAG into tools like chatbots becomes a game-changer.
RAG powered chat bot
A common challenge that many businesses face is the volume of unstructured data in their processes – this might be because they deal with large volumes of lengthy documents, like manuals, guides or contracts. And searching for the right information manually in this unstructured data can take up an awful lot of time. Integrating RAG with a user-friendly chatbot interface allows users to quickly and efficiently retrieve information, which can transform workflows, enhance productivity and boost their job satisfaction.
What use cases can they solve?
RAG-powered chatbots can revolutionize a variety of business applications, such as:
- Tender proposals: RAG streamlines the preparation process by extracting and consolidating relevant information, allowing managers to focus on strategy and higher-level tasks.
- Customer support: by analysing customer interactions and retrieving pertinent documents in real time, RAG empowers agents with quick, accurate responses, enhancing customer satisfaction.
- Report writing: RAG can assist in generating detailed, high-quality reports, improving efficiency and accuracy.
While LLMs undoubtedly have impressive conversational capabilities, their limitations do stop them from being very effective in critical business processes. RAG can be a good solution, grounding AI responses in relevant information, making it much more reliable for business applications. By integrating RAG, businesses can enhance their decision-making and streamline processes with greater confidence, thus maximising the potential of generative AI.
If you would like to speak to Valcon about integrating RAG into your gen-AI programmes, then please get in touch with [email protected] and [email protected]
