{"id":598515,"date":"2024-07-26T11:11:25","date_gmt":"2024-07-26T05:41:25","guid":{"rendered":"https:\/\/www.digit.in\/?p=598515"},"modified":"2024-08-01T11:33:12","modified_gmt":"2024-08-01T06:03:12","slug":"llm-to-rag-decoding-ai-jargon-matters-heres-why","status":"publish","type":"post","link":"https:\/\/www.digit.in\/features\/general\/llm-to-rag-why-decoding-ai-jargon-matters.html","title":{"rendered":"LLM to RAG: Decoding AI jargon matters, here\u2019s why"},"content":{"rendered":"\n

Whenever a new facet of technology debuts and gathers momentum, it introduces lots of new terminology into the lexicon. From tuning<\/em> into FM radio to streaming<\/em> music on-the-go, STD<\/em> phone calls in the past to emoji and GIF<\/em>-enabled chats in your favourite app, for example, haven\u2019t we all learned, unlearned, and relearned quite a few tech terms over the years? Now it\u2019s AI\u2019s turn to introduce a whole new wave of words for us to throw around. <\/p>\n\n\n\n

Naturally, as the use of AI proliferates, so does its related jargon. It\u2019s important to address some of that jargon to avoid unnecessary confusion and to save our own sanity. I think there have been too many acronyms floating around and key concepts unexplained as far as AI is concerned. I\u2019ve been guilty of not paying enough attention to it, something that I\u2019m trying to correct through this article. Better late than never, right?<\/p>\n\n\n\n

Also read: How RAG boosts LLM accuracy and reduces AI hallucination<\/a><\/p>\n\n\n\n

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Take LLM or Large Language Models, for instance \u2013 the reason behind the current AI renaissance we are all experiencing. It\u2019s the backbone of AI chatbots like OpenAI\u2019s ChatGPT, Microsoft Copilot, or Google Gemini, but how many of us had even heard of such a term called LLM even five years ago? Simply put, it\u2019s an AI algorithm trained on vast amounts of text and natural language-related data to generate textual output similar to a human would. But what about text-to-image generation services like Midjourney, Stable Diffusion, or DALL-E? They use LLMs for language demystification, but they\u2019re strictly AI diffusion models \u2013 machine learning algorithms that progressively add noise to data and learn to reverse the process, especially useful for generating images, videos, and sound. LLMs and diffusion models make up most of the Generative AI products and services you\u2019ve seen and used since 2022. It\u2019s not even been two years since ChatGPT 3 blew the AI floodgates open for the general public, and we\u2019ve already abbreviated Generative AI to GenAI. Keeping it snappy!<\/p>\n\n\n\n

Then there are slightly more fundamental concepts of AI that are good to know, especially in the wake of AI PCs. Whether it\u2019s today or in the near future, conversations on AI PC<\/a> or AI Laptop won\u2019t be complete without the mention of TOPS \u2013 something you must\u2019ve seen across all the recent AI hardware-related articles here in the magazine or on Digit.in. TOPS, or Tera Operations Per Second, is a measure of how many trillion operations a processor can perform every second. In the context of AI hardware, TOPS is a benchmark of processing power, particularly relevant for tasks involving deep learning and neural networks. Higher TOPS indicate a more capable processor, one that can handle complex AI computations with ease. As we start reviewing and writing about AI Laptops, you\u2019ll see TOPS being mentioned as a key parameter going forward.<\/p>\n\n\n\n