Build Your Business-specific LLMs Using RAG
Kashif Manzoor
Enabling Customers for a Successful AI Adoption | AI Tech Evangelist | AI Solutions Architect
When we talk about Large Language model implementations in the business context, you will hear the widespread term Retrieval-Augmented Generation (RAG), and it is being presented as the Magic wand to several scenarios where you need to rely on your data while using the generative AI. RAG is the solution for assembling your business data and the LLM; you will get the desired outputs.
So, I thought of going through the fundamentals of RAG; it is just for understanding and clarity. In a paper in 2020, "Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks," Meta introduced a retrieval-augmented generation framework to give LLMs access to information beyond their training data. RAG allows LLMs to build on a specialized body of knowledge to answer questions more accurately.
Retrieval-augmented generation (RAG) in Large Language Models (LLMs) enhances the model’s ability to generate responses by dynamically retrieving relevant information from a large dataset or database at the time of the query. This approach combines the generative power of LLMs with the specificity and accuracy provided by external data sources, enabling the model to produce more accurate, detailed, and contextually relevant outputs.
How RAG Works:
Example:
Suppose you are using a RAG-enhanced LLM for a medical information system. A user asks, “What are the latest treatment options for type 2 diabetes?”
Without RAG, an LLM would have to rely solely on the information it was trained on, which might be outdated or lack the specific details in newly published research. RAG ensures the model’s output is current and deeply informed by the most relevant available data, significantly enhancing the quality and utility of the response.
What are the use cases for RAG (Retrieval-Augmented Generation)?:
These use cases demonstrate the versatility and potential of RAG to transform information retrieval and interaction within organizations. In the next week, I will go through the technical aspects of the RAG and how it works.
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Until next week,
Kashif Manzoor
The opinions expressed here are solely my conjecture based on experience, practice, and observation. They do not represent the thoughts, intentions, plans, or strategies of my current or previous employers or their clients/customers. The objective of this newsletter is to share and learn with the community.
AI in Education and Learning Expert, Creator of the world's first publicly available AI teacher, Upskilled 47,000 learners globally, Multiple Award recipient including from the Prime Minister of UK and 30 under 30 (Mint)
1 年RAG is efficient. Data igestion and prompt curation are key to a good output.
Co-Founder of Altrosyn and DIrector at CDTECH | Inventor | Manufacturer
1 年RAG's approach to building customized AI models aligns with the ongoing trend of tailoring large language models (LLMs) for specific business needs. This customization, tapping into real-time data, reflects the growing emphasis on precision and efficiency in AI applications. Looking back, historical data often showcases the evolution of AI customization, but how do you see RAG's methodology addressing potential challenges such as ethical considerations and bias in real-time data integration? Exploring these facets could provide valuable insights into refining AI models for responsible and inclusive deployment.