RAG vs. semantic search - what's the difference?
Artificial intelligence has significantly changed the way we search for and present information. With RAG (Retrieval-Augmented Generation), we can combine the capabilities of generative AI with a company’s internal databases to answer questions based on its own resources. Semantic search, on the other hand, focuses on finding information not just by keywords, but by the meaning of the query. Although both approaches often go hand-in-hand, it’s important to highlight the major differences. So what exactly sets RAG apart from semantic search?
What is semantic search?
Semantic search is a method of information retrieval that focuses on understanding the meaning and context of user queries rather than merely matching keywords. In practice, this means the system analyzes the intent behind the question, considers word dependencies, and the thematic context of the content. This leads to more accurate and relevant results, even when queries are imprecise or ambiguous.
This technology relies on artificial intelligence, natural language processing, and machine learning. It allows search systems to recognize synonyms, interpret word ambiguity, and better understand user intent, approaching a human-like understanding of language. As a result, the search process becomes more natural, greatly improving the effectiveness of search engines.
Thus, semantic search goes a step beyond traditional keyword-based search — instead of focusing solely on the words, the system analyzes the meaning and context of the question. It recognizes key elements such as people, places, or concepts and checks how they relate to each other, for example using knowledge graphs. This enables it to find and clearly present an already existing, precise answer.
In that sense, semantic search is like an experienced librarian who can find exactly the book or document we need. But if we stick with that analogy, RAG isn’t just an experienced librarian — it’s an expert who not only brings the right book but also summarizes its most important insights in a clear and understandable way.
What is RAG search?
RAG (Retrieval-Augmented Generation) is a modern approach in artificial intelligence that combines two main processes: retrieving information from external sources and generating answers using large language models (LLMs). Unlike classic models that operate solely on data learned during training, a RAG system first retrieves relevant content and then uses it as context to generate a response.
The first step — retrieval — involves processing the user’s query and automatically finding the most relevant data fragments, whether from structured or unstructured sources like reports, documentation, or articles. In the next step — augmentation — the collected information is passed to the language model, which uses it as additional context to generate a response. This allows AI to go beyond what it “knows” from training and tap into current and contextual sources of knowledge.
The main advantage of RAG is the higher relevance, timeliness, and clarity of generated content. The user receives not only an answer based on the latest data but also the ability to verify its sources. This makes RAG-based solutions particularly useful in applications like business chatbots, knowledge management systems, or specialized search engines, where it is crucial to limit misinformation and access trustworthy data in real time.
Semantic search vs. RAG – key differences
| Aspect | Semantic search | RAG (Retrieval-Augmented Generation) |
|---|---|---|
| Main function | Finds and returns data fragments most semantically relevant to the query. Goal: deliver existing, precise answers quickly. | First finds relevant fragments (as in semantic search), then passes them to an LLM that generates a coherent, tailored response. |
| Technology | Converts queries and documents into numerical vectors (embeddings) and compares them based on similarity. | Combines semantic search (embeddings) with an LLM that creates a new, tailored response from the retrieved content. |
| Process steps | Convert query to vector (embedding). Compare with document vectors. Return most relevant content. |
Retrieve relevant content. Add it as context to the query. Generate a complete response using an LLM. |
| Output to user | Existing fragments, quotes, documents (no synthesis or rephrasing). | New, synthesized response that may combine, rephrase, and explain retrieved content. |
| Use cases | FAQs, catalogs, helpdesks, fast document search, knowledge bases. | Chatbots, assistants, report summaries, business answers from various sources. |
| Personalization | None — answers limited to already existing content. | Possible — LLM can generate context-aware, personalized responses based on user data or session. |
| Data freshness | Limited to indexed database; does not use external data. | Can use the latest, dynamically retrieved data without retraining the model. |
| Cost and efficiency | Low resource costs, fast response time, simple architecture. | Higher costs (LLM), more resources, slightly longer response time due to multi-step pipeline. |
| Response flexibility | Limited — user gets exact fragments without synthesis. | High — LLM can explain, summarize, or merge info from multiple sources tailored to the query. |
| Example workflow | Search “remote work policy” — get HR document fragments. | Ask “What are my remote work options this quarter?” — system retrieves documentation, considers seniority, generates summary. |
Summary
As we can see, semantic search focuses on finding the most relevant existing information based on the meaning of the query, whereas RAG takes it a step further — combining semantic search with a generative language model to produce coherent and contextual answers from retrieved data. The former is ideal for fast document access, the latter for complex answers crafted in real time.
Implementing Retrieval-Augmented Generation brings organizations to a whole new level of knowledge management and utilization. With RAG, you can leverage existing GenAI models together with your internal documentation — without the need for expensive retraining. And when deployed in the AWS Cloud, RAG becomes even easier and more convenient to implement, with significantly shorter time to value.
Want to implement RAG technology using AWS Cloud in your organization? Reach out to our experts and embrace GenAI today! Contact us at kontakt@lcloud.pl
