Optimizing the LLMs Output: An Introduction to RAG (Retrieval-Augmented Generation)
Lately, I have been diving deep into GenAI, LLMs and AI Agents, exploring how these technologies can be integrated into our workflows and day-to-day operations. It’s becoming clear that sooner or later, we all need to understand and incorporate AI in some way to be relevant, efficient and up-to-date in the market.
What’s engaging is how often terms like RAG (Retrieval-Augmented Generation) and Fine-Tuning come up in discussions around GenAI and LLMs. These techniques are at the core of optimizing AI performance, making them important to learn if you’re navigating the AI space.
Let’s explore RAG and how it can be used to unlock the full potential of Large Language Models (LLMs).
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What are LLMs?
Before we dive deeper, let’s quickly understand what LLMs are. LLMs are a type of AI model that uses natural language processing (NLP) to generate human-like text based on input prompts. They’ve transformed the way we interact with machines and have numerous use cases in areas like chatbots, content generation, and more.
However, despite their capabilities, LLMs frequently struggle with providing accurate…
