Traditional RAG technology, which simply retrieves document fragments, often misses context, but AI is now evolving based on deeper reasoning and understanding.
Imagine this: You show a very important company contract to an AI and ask, “What are the core clauses of this contract?” The AI provides an answer confidently, but it completely ignores the “appendix” part of the contract that serves as the basis for that answer, leading to a completely incorrect conclusion. It is similar to how we might get an answer wrong when studying for an exam by only reading the main text of a textbook and skipping the important tables or reference materials.
Recently, it has been pointed out that RAG (Retrieval-Augmented Generation), one of the technologies in generative AI, can make these kinds of mistakes. People often say, “The AI is lying” or “It is experiencing hallucinations.” Why does this happen?
Why is this important?
We now live in a stage where we go beyond simply asking AI to “write something” and ask it to “read my work materials and answer.” RAG is the technology that helps AI reference your documents. However, if an AI fails to properly understand the documents and retrieves information superficially, serious errors can occur in business decisions or important judgments.
Rather than blindly trusting AI’s output, knowing how the AI “retrieves” information is becoming a core competency for individuals and companies in this era.
Easy to understand: AI’s ‘open-book test’
In simple terms, ‘RAG’ is a technology that makes AI take an ‘open-book test.’ Instead of relying only on the existing knowledge it learned itself, it allows the AI to keep the documents we provided open like a reference book and find answers.
| However, the ‘traditional RAG’ we have been using was a bit simple. Experts explain that this is like treating documents as ‘a bag filled with random text fragments.’ [Why YourRAGMayBeLyingtoYouand How to Fix It | LinkedIn](https://www.linkedin.com/posts/udaykiranchimpiri_ai-machinelearning-rag-activity-7450764888043331585-rqFy) |
| Shall we take an example? Suppose you instructed the AI, “For more detailed information on this matter, look at Appendix B at the back.” However, traditional RAG often ignores the crucial instruction to “Look at Appendix B” because it mathematically only retrieves text fragments that ‘look’ most similar based on word frequency or similarity. It is like arriving at the wrong destination when finding your way because you followed the footprints of the person in front of you instead of looking at the signposts. [Why YourRAGMayBeLyingtoYouand How to Fix It | LinkedIn](https://www.linkedin.com/posts/udaykiranchimpiri_ai-machinelearning-rag-activity-7450764888043331585-rqFy) |
Current situation: Evolving technology
| Some say, “RAG is a thing of the past now.” However, the thoughts of engineers who actually build AI systems in the field are different. They are actually focusing more on and increasing investment in RAG technology. [WhyRAGMightActually Matter More Than Ever In2025 | HackerNoon](https://hackernoon.com/why-rag-might-actually-matter-more-than-ever-in-2025) |
| It is not that the technology is dead, but that the initial, simple form of ‘vector search’ (a method of finding similar text) has reached its limit. We are now moving beyond the stage of simply ‘finding’ documents to the stage where AI ‘understands and reasons’ about them. [AI document analysis technology that even the Board of Audit and Inspection can utilize | Meaning-based… - YouTube](https://www.youtube.com/watch?v=IH3_026P8GE) |
What will happen in the future?
AI technology will become much smarter. Future RAG systems will not just throw out paragraphs containing related words, but will accurately grasp the user’s intended meaning behind the question and derive logical conclusions within the context of the entire document.
We must now become ‘wise users’ who know how to question the basis of information rather than blindly trusting the information AI retrieves. Technology is already evolving, and those who notice those changes first will be able to enjoy the AI era more flexibly.
MindTickleBytes AI Reporter’s View
There is no need to be disappointed just because RAG sometimes says something erratic. That is a kind of growing pain that AI experiences in order to attain true ‘intelligence.’ As AI is reborn from a machine that simply searches documents into a true ‘assistant,’ it is time for us to strengthen our critical thinking muscles to verify AI’s answers.
References
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[Why YourRAGMayBeLyingtoYouand How to Fix It LinkedIn](https://www.linkedin.com/posts/udaykiranchimpiri_ai-machinelearning-rag-activity-7450764888043331585-rqFy) -
[AI document analysis technology that even the Board of Audit and Inspection can utilize Meaning-based… - YouTube](https://www.youtube.com/watch?v=IH3_026P8GE) -
[WhyRAGMightActually Matter More Than Ever In2025 HackerNoon](https://hackernoon.com/why-rag-might-actually-matter-more-than-ever-in-2025)
- The amount of documents is too large
- It simply retrieves similar text fragments and misses context
- AI responds too slowly
- Determined the technology is dead and gave up
- No longer making improvements
- Enhancing the technology for deeper understanding and reasoning beyond simple search
- AI only reading documents quickly
- AI reading, understanding, and reasoning about documents
- AI writing documents by itself