Introduction: Are You Also Mixing Them Up?
Have you ever seen or used expressions like “searching with RAG” or “enhancing search functionality with RAG”?
In reality, these expressions contain a fundamental misunderstanding. Search and RAG are technologies with completely different purposes, and confusing them leads to inappropriate technology selection.
This article untangles this confusion that many engineers fall into and provides an essential understanding to properly distinguish between these technologies.
The Beginning of Confusion: The Misunderstanding That “RAG’s Retrieval is Search”
The first step of RAG (Retrieval-Augmented Generation) is “Retrieval.”
Many explanations describe this step as “searching for and retrieving relevant information from external databases or knowledge bases.”
This expression “searching for” is the root of all confusion.
Why Does This Cause Confusion?
When you use the word “search,” you imagine the act of “looking for information” like Google search or site search.
However, RAG’s Retrieval is not strictly “search” but “extraction.” It’s a process of extracting related information through similarity calculations from a pre-vectorized database.
The Essential Difference: Completely Different Purposes
The Purpose of Search
Finding the information itself
- “Where is the server configuration manual?” → Looking for the document itself called the manual
- “Where is the October 2023 incident report?” → Finding the file called the report
- The goal is to identify where the information is located
The Purpose of RAG
Generating answers based on information
- “How to handle this error?” → Deriving solutions based on multiple manuals and past cases
- “What are the incident trends in 2023?” → Analyzing trends based on multiple reports
- The goal is to create new response text
The Decisive Difference: Direct vs. Indirect
To put it even more clearly, it can be organized as follows:
Search = When you want direct information
Used when the answer is written directly in some document and you want to find that document.
RAG = When you want indirect answers
Used when the answer is not written directly in a single document and needs to be created by integrating and inferring from multiple information sources.
Understanding Through Concrete Examples
IT Department Handover Scenario
When to use search:
- “Where is the server setup procedure document?”
- → Search in Box, SharePoint, Confluence, etc.
- → “server_setup.pdf” is found
- → Open the document and check the details
When RAG is appropriate:
- “What lessons can we learn from server troubles over the past 3 years?”
- → Extract information from multiple trouble reports
- → AI integrates common patterns and lessons to generate an answer
- → More efficient than reading each original document one by one
Medical AI Example
Search:
- “What are the side effects of this drug?” → Directly stated in the package insert
RAG:
- “What diseases can be considered from this combination of symptoms?” → Requires indirect inference from multiple medical literature
Why “Searching with RAG” is Wrong
This expression is technically contradictory.
RAG cannot replace search. Search and information retrieval are just used inside RAG.
Correct expressions:
- “Search first, then process with RAG”
- “Retrieve information in RAG’s Retrieval step”
- “Integrate search results with RAG”
Practical Decision Criteria
When you’re unsure which to use in your project, ask this question:
“Does the user want the information itself, or an answer based on the information?”
- Information itself (documents, files, articles) → Search
- Answers based on information (integration, summary, inference) → RAG
Summary
Search and RAG may seem similar, but they have completely different purposes.
- Search = Technology for finding information
- RAG = Technology for generating answers from information
If you think “enhance search with RAG” without understanding this essential difference, you’ll make wrong technology choices and take on unnecessary costs and complexity.
Using the appropriate technology in the appropriate situation is the first step in effective system design.
Does your project really need RAG? Or would a good search system be sufficient?
Supplement: Understanding the Risks Behind RAG’s “Generation”
Unlike search systems, RAG does not simply show information – it generates answers based on information.
However, this generation process is probabilistic and does not guarantee correctness.
Even when the source documents are accurate, the model may produce incorrect conclusions during integration or summarization.
Therefore, when adopting RAG, it is essential to implement mechanisms that verify AI-generated answers and present their supporting evidence.
RAG should be properly positioned not as a replacement for search, but as a reasoning-assistive technology that complements it.