Discover how to understand RAG (Retrieval-Augmented Generation), a core technology that helps overcome the knowledge limitations of AI models and utilize the latest information.
Imagine this: You have business manuals or vast personal notes that you have carefully organized over several years. You ask a popular AI chatbot a question based on this valuable material, but how frustrating would it be if the AI replied, “I’m sorry, I don’t know that”?
Large Language Models (LLMs)—AI that generates natural language by training on massive amounts of text data—are inherently limited to information within the scope of the public internet data they have trained on. Reference 4 In other words, your unique data is ‘uncharted territory’ to the AI. The technology that emerged to solve this problem is RAG (Retrieval-Augmented Generation).
Why is this important?
RAG is currently the most widely used technology for improving the response quality and accuracy of AI models. Reference 4 It is essential when dealing with information that AI models cannot learn in advance, such as internal documents used in corporate settings or real-time news. Reference 5
Simply put, it’s like allowing a student (AI) taking an exam to use a reference library (external database) to their heart’s content, rather than just giving them a textbook (training data). Metaphorically, it is a technology that turns a test into an open-book one when the AI would otherwise have to rely solely on its memory. This allows the AI to provide much more accurate and reliable answers. Reference 5
Easy Understanding: Creating a Reference Room for AI
Many RAG tutorials simply bring in completed frameworks and say, “You’re done after calling these three functions.” However, learning this way makes it easy to miss the core principles of the technology. Reference 2 To truly understand the technology, the experience of building the structure ‘from scratch’ is important. Reference 11
The process of building RAG proceeds in the following general order:
- Document Preparation: Extract PDF or document files into text that the AI can read.
- Chunking: Break long documents into small pieces that are easy for the AI to process at once.
- Embedding Generation: Convert text fragments into coordinate-based numeric vectors that the AI can understand.
- Similarity Search: When a user asks a question, the AI finds the content most similar to the question among the data fragments.
- LLM Answer Generation: Deliver the found information to the AI model along with the question so that the model can complete the final answer based on it. Reference 7
For example, when we try to find specific information in a thick encyclopedia, it is exactly the same way we manually look through an index (search), find the relevant page, summarize it, and tell a friend.
Current Situation: How far have we come?
Today, RAG technology has reached a very practical stage. You can configure automated workflows using simple No-code tools without knowing how to code, Reference 1 or use programming languages like Python to implement it precisely from start to finish. Reference 16
However, beginners sometimes get confused due to complex environment settings or outdated tutorials rather than the latest information. Reference 16 Also, as the amount of data increases, accuracy can drop with simple searches, so various advanced technologies are constantly evolving, such as optimizing search scope or managing context length. Reference 10
What will happen in the future?
In the future, RAG technology will move in a direction that anyone can easily access and implement. Those who have learned the basic principles by implementing everything from scratch will have a much more solid foundation when designing larger-scale corporate systems later on. Reference 20 Start a small project right now. The experience of building it yourself will be the fastest key to understanding the AI era. Reference 15
MindTickleBytes AI Reporter’s Perspective
RAG is the key that transforms AI from a simple knowledge repository into a ‘smart assistant’ by our side. This journey of making AI read and answer our documents might be the smallest yet biggest change that alters how we search for and handle information itself.
References
- Build a RAG Chatbot in n8n — Easiest Way to Embed AI… - YouTube, https://www.youtube.com/watch?v=D_Epp2UmQAk
- Retrieval Augmented Generation from Scratch Tutorial, https://joescharf.com/retrieval-augmented-generation-from-scratch-tutorial/
- Retrieval Augmented Generation (RAG) - DeepLearning.AI, https://learn.deeplearning.ai/courses/retrieval-augmented-generation/lesson/rrngb/a-conversation-with-andrew-ng
- What is Retrieval-Augmented Generation (RAG)?, https://www.analyticsvidhya.com/blog/2023/09/retrieval-augmented-generation-rag-in-ai/
- Understanding RAG by Building a ChatPDF App… - DEV Community, https://dev.to/sharathkurup/understanding-rag-by-building-a-chatpdf-app-from-numpy-to-faiss-part-2-33oe
- Understanding RAG Part V: Managing Context Length, https://machinelearningmastery.com/understanding-rag-part-v-managing-context-length/
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GitHub - linga009/causal-graph-rag Linga Murthy Narlagiri, https://www.linkedin.com/posts/lingam2620121989_github-linga009causal-graph-rag-activity-7475187703802679296-tdjm - RAG Crash Course for Beginners - YouTube, https://www.youtube.com/watch?v=swvzKSOEluc
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Build RAG From Scratch in Python Modern RAG Project for Beginners …, https://www.youtube.com/watch?v=mMb_0cqw-E8 - Building RAG Systems: From Zero to Hero - DEV Community, https://dev.to/gautamvhavle/building-production-rag-systems-from-zero-to-hero-2f1i
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- It shortens AI training time
- It helps AI generate answers based on data it has not been trained on
- It reduces the size of AI models
- Extracting text from documents and creating chunks
- Generating embeddings
- Retraining the AI model
- Because the model finds the answer by itself
- Because it can search for and utilize the latest information or personal data outside of its training data
- Because the AI model calculates faster