Cooking is one of my favorite hobbies, so I took the opportunity to combine my interest in technology and cooking to discuss this innovative chapter on Generative AI, RAG. First, let’s get the basics out of the way.
What is Generative AI? How is it different from the AI?
Artificial Intelligence (AI)
AI refers to computer systems performing tasks that typically require intelligence. It includes machine learning (ML) and deep learning (DL) methods. AI systems learn from data, identify patterns, and make decisions autonomously. Examples of AI applications include speech recognition and self-driving cars. While AI can simulate thinking processes, it does not have human consciousness.
Generative AI
Generative AI is a subset of AI that focuses on generating content. In contrast to AI, which leads by rules, generative AI employs self-learning models to produce innovative outputs. Examples of AI encompass text generation models like GPT 4 and image creation models like DALL E. This branch of AI merges creativity with innovation, empowering machines to create art, music, and literature. Nonetheless, it encounters challenges related to considerations, bias mitigation, and control over the generated content.
Generally speaking, AI techniques encompass applications, while generative AI stands out for its emphasis on creativity and original content creation.
Now, back to our test kitchen. Let’s put on our virtual aprons to travel on a flavor-filled journey to understand how RAG (Retrieval-Augmented Generation) works in the realm of Generative AI. Imagine we’re in a busy kitchen, aprons on, ready to cook some insights. Let’s use a basic roasted chicken recipe for this analogy.
The Recipe
Ingredients:
- Chicken is our base ingredient, representing the raw text or prompt. I recommend cleaned chicken (unbiased).
- Seasonings: These are the retrieval documents, like a well-stocked spice rack. Each seasoning (document) adds depth and context to our chicken (prompt).
Preparation:
- Marinating the Chicken: We start by marinating our chicken with the prompt. This is where RAG comes into play. It retrieves relevant documents (seasonings) from its vast knowledge base (like a library pantry).
- Selecting the Right Spices: RAG carefully selects the most relevant documents (spices) based on the prompt. These could be scientific papers, blog posts, or historical texts. This is my favorite part.
Cooking Process:
- Simmering and flavor injecting: Just as we simmer our chicken with spices, RAG injects the prompt with context from the retrieved documents. It absorbs the flavors of knowledge, understanding nuances, and connections.
- Balancing Flavors: RAG balances the richness of retrieved information. Too much spice (document) overwhelms the dish (response), while too little leaves it bland.
Generative Magic:
- The Cooking Alchemy: Now, the magic happens. RAG combines the marinated prompt with the seasoned context. It’s like a chef creating a new recipe, drawing inspiration from old cookbooks of classic dishes.
- Creating the Dish: RAG generates an informed and creative response. It’s not just recycling facts; it’s crafting a unique flavor profile.
Serving the Dish:
- Plating and Garnishing: Our dish is ready! RAG delivers a rich, layered, and tailored response to the prompt—like presenting a beautifully plated meal.
- Bon Appétit!: The user enjoys the response, savoring the blend of retrieval and generation. Just as a well-seasoned chicken satisfies the palate, RAG satisfies the hunger for knowledge and creativity.
RAG reminds me of a beautiful meal that can satisfy the desires of the most discerning taste. A traveled chef who searches for the best ingredients from around the globe and retrieves them to generate tasteful dishes. So, next time you encounter RAG, think of yourself as a chef creating delightful technology-based feasts.
A curated list of retrieval-augmented generation (RAG) in large language models.
