LLM Apps in Production (RAG + Vector DB + Caching) · 강의

간단한 RAG 파이프라인 구축하기

데이터 수집부터 선택한 LLM을 사용한 응답 생성까지 기본 RAG 작업 흐름을 구현합니다.

간단한 RAG 파이프라인 구축하기은(는) CoddyKit의 무료 LLM Apps in Production (RAG + Vector DB + Caching) 강의입니다. 이것은 4개 중 3번째 강의입니다. 아래에서 전체 강의를 무료로 읽을 수 있으며, 내장 코드 에디터와 24/7 AI 튜터와 함께 브라우저에서 직접 실습할 수 있습니다. 이 강의는 LLM Apps in Production (RAG + Vector DB + Caching) 학습 경로의 일부이며, 진행 상황이 웹과 CoddyKit 앱에 동기화됩니다. LLM Apps in Production (RAG + Vector DB + Caching) 강의에는 총 4개의 강의가 포함되어 있습니다.

이 강의의 일부는 아직 번역되지 않았으며 영어로 표시됩니다.

Intro to RAG Pipelines

You've learned what RAG is and why it's powerful. Now, let's build one! A Retrieval Augmented Generation (RAG) pipeline is a sequence of steps that combine an LLM with external data.

Its main goal is to give LLMs up-to-date, factual information, reducing "hallucinations" and improving response quality.

Understanding the RAG Flow

Think of a RAG pipeline as having two main phases: preparation and querying. First, you get your data ready. Then, when a user asks a question, your system finds relevant info and uses it to help the LLM answer.

Preparation: Ingest & Index Data
Querying: Retrieve & Generate Response

Step 1: Prepare Your Knowledge Base

Before an LLM can use your data, it needs to be processed. This involves:

Loading: Getting data from various sources (PDFs, websites, databases).
Chunking: Breaking large documents into smaller, manageable pieces (chunks). A chunk might be a few sentences or a paragraph.

Smaller chunks are easier to search and fit into an LLM's context window.

Step 2: Turn Chunks into Embeddings

How do we "search" text semantically? We turn it into numbers! An embedding model converts each text chunk into a list of numbers called a vector embedding.

These vectors capture the meaning of the text. Chunks with similar meanings will have vectors that are "close" to each other in a mathematical sense.

Step 3: Store for Fast Retrieval

Once you have vector embeddings for all your chunks, you need to store them efficiently. A vector store (or vector database) is specialized for this.

It allows for very fast "similarity search" – finding vectors that are closest to a given query vector. This is key for quickly retrieving relevant information.

Processing a User Query

When a user types a question, your RAG pipeline springs into action. The first thing that happens is that the user's query itself is converted into a vector embedding.

This query embedding will then be used to search your stored data for relevant information.

Step 4: Find the Best Matches

With the user query's embedding, the RAG system performs a similarity search in your vector store. It looks for data chunks whose embeddings are most similar to the query's embedding.

The most similar chunks are considered the most relevant "context" for answering the user's question.

Step 5: Enhance the LLM's Prompt

Now, we combine the user's original question with the retrieved context. This creates an augmented prompt.

Instead of just asking, "What is X?", the prompt becomes something like: "Given this information: [retrieved chunks], what is X?"

This guides the LLM to use the provided facts.

Step 6: LLM Generates the Answer

Finally, the augmented prompt is sent to the Large Language Model. The LLM processes both the user's question and the retrieved context.

It then generates a response that is grounded in the factual information provided by your data, rather than relying solely on its pre-trained knowledge.

Visualize the RAG Steps

Here's a conceptual Python example showing the flow. Imagine load_data, chunk_text, create_embeddings, index_embeddings, search_vector_store, and generate_llm_response are functions you'd implement.

Try running this example to see the sequence!

public class Main {
  public static void main(String[] args) {
    System.out.println("1. User query received: What is RAG?");

    // Simulate embedding the query
    String queryEmbedding = "Embedding for 'What is RAG?'";
    System.out.println("2. Query embedded: " + queryEmbedding);

    // Simulate retrieving relevant chunks from a vector store
    String[] retrievedChunks = {
      "Chunk 1: RAG helps LLMs use external facts.",
      "Chunk 2: Vector databases store embeddings."
    };
    System.out.println("3. Retrieved relevant chunks: " + String.join(", ", retrievedChunks));

    // Simulate augmenting the LLM prompt
    String augmentedPrompt = (
      "Based on the following context:\n"
      + String.join(" ", retrievedChunks) + "\n\n"
      + "Answer the question: What is RAG?"
    );
    System.out.println("4. Augmented LLM prompt created.");

    // Simulate LLM response generation
    String llmResponse = (
      "RAG pipelines enhance LLMs by providing external, "
      + "factual context from stored documents, which helps "
      + "reduce hallucinations and improve accuracy."
    );
    System.out.println("5. LLM generated response.");

    System.out.println("\nFinal Answer: " + llmResponse);
  }
}

RAG Pipeline Quiz

Which of the following accurately describes the correct order of steps when a user submits a query in a RAG pipeline?

Recap: Building RAG

Great job! You've now grasped the full flow of a basic RAG pipeline. We covered:

The preparation steps: ingesting, chunking, embedding, and indexing your data.
The querying steps: embedding the user query, retrieving context, augmenting the prompt, and generating a response with the LLM.

This foundational understanding will help you build more robust LLM applications!

자주 묻는 질문

“간단한 RAG 파이프라인 구축하기” 강의는 무료인가요?

네 — “간단한 RAG 파이프라인 구축하기” 전체 내용을 이 웹사이트에서 무료로 읽을 수 있습니다. 인터랙티브하게 실습하려면(내장 코드 에디터와 24/7 AI 튜터), CoddyKit PRO로 업그레이드하면 LLM Apps in Production (RAG + Vector DB + Caching) 강의 전체를 잠금 해제할 수 있습니다. LLM Apps in Production (RAG + Vector DB + Caching) 강의에는 총 4개의 강의가 포함되어 있습니다.

“간단한 RAG 파이프라인 구축하기”에서 뭘 배우나요?

데이터 수집부터 선택한 LLM을 사용한 응답 생성까지 기본 RAG 작업 흐름을 구현합니다. 브라우저에서 직접 실행하는 실습 코드로 LLM Apps in Production (RAG + Vector DB + Caching)을(를) 배우며, 24/7 AI 튜터가 강의를 진행하면서 질문에 답변해줍니다.

LLM Apps in Production (RAG + Vector DB + Caching)을(를) 시작하는 데 경험이 필요한가요?

사전 경험은 필요하지 않습니다. CoddyKit의 LLM Apps in Production (RAG + Vector DB + Caching)은(는) 초급자부터 고급 학습자까지를 위해 구성되어 있으므로, 여기서 시작하거나 처음부터 시작할 수 있으며 자신의 속도대로 진행할 수 있습니다. 이것은 4개 중 3번째 강의입니다.

“간단한 RAG 파이프라인 구축하기” 강의는 얼마나 걸리나요?

대부분의 CoddyKit 강의는 약 5~10분이 소요됩니다. 각 강의는 간결하고 인터랙티브하여 꾸준한 진행이 가능하며, 웹과 앱에서 중단한 부분부터 바로 시작할 수 있습니다.

이 LLM Apps in Production (RAG + Vector DB + Caching) 강의에서 코드를 작성하고 실행할 수 있나요?

네. 모든 LLM Apps in Production (RAG + Vector DB + Caching) 강의에는 내장 코드 에디터가 포함되어 있으므로, 브라우저에서 바로 실제 코드를 작성하고 실행한 후 즉시 AI 피드백을 받을 수 있습니다 — 로컬 설정이 필요 없습니다.

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