Unlock the full potential of AI models with two game-changing techniques.

↳Large language models are pre-trained with Gigabits of data. ↳With this pre-training, the LLMs are not used widely to retrieve information. ↳Instead, there are two important techniques (RAG and/or Fine-Tuning) language models are powered with. Let's understand about both RAG and Fine-tuning now.1️⃣ What is RAG?Natural Language Processing (NLP) uses Retrieval Augment Generate (RAG) to improve the performance of the language models.↳ Retrieval: The model retrieves relevant information from a large knowledge base.↳ Augment: The retrieved information is combined with the input text to create an augmented input.↳ Generate: The model generates an output based on the augmented input.✳ In simple terms, any LLM which can retrieve information about current data from external sources or databases, that LLM is powered with RAG.Let's say you ask a RAG model, "What is the capital of Canada?" ↳ The model would first retrieve information about Canada from its knowledge base, such as "Ottawa is the capital of Canada." ↳ It would then augment the input with this information, and finally generate an output like "The capital of Canada is Ottawa.”But, Why RAG is required?➡ RAG-based LLMs are crucial for information which requires up-to-date information. ➡ Imagine a doctor using a Non-RAG AI model (which has a high probability of outdated information) to prescribe medicine to patients, the results will be detrimental.➡ Medical Queries are the best example which requires RAG-powered AI models.✳ Google’s Gemma is a RAG-powered LLM model.2️⃣ What is Fine-Tuning?↳ Pre-trained LLMs which are trained specifically for a specific domain or task.↳ When a pre-trained LLM is fine-tuned for a specific domain like a legal domain to answer any legal queries, then the LLM is powered with fine-tuning.✳ OpenAI’s GPT-3 fine-tuned on various domains like legal documents and medical text.So now we know the basics of both RAG and Fine-tuning techniques.Can an LLM be used with both techniques? ↳ Yes, there are LLMs which are powered with both techniques. Below are a couple of examples. ↳ Vectorize's Hybrid RAG-Fine-Tuning Model ↳ MatrixFlows' Knowledge-Augmented LLM:In conclusion, RAG and Fine-Tuning are essential techniques for unlocking the full potential of LLMs and enabling them to tackle a wide range of real-world applications.By combining these techniques and leveraging the strengths of each approach, we can create more powerful, adaptable, and specialized LLMs that can drive innovation and progress across various domains. 🔥 

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