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➡ Imagine a doctor using a Non-RAG AI model (which has a high probability of outdated information) to prescribe medicine to patients, the results will be detrimental.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. 🔥 

#ai #artificialintelligence #llm #RAG #fine-tuning #gemma #gpt-3 #openai