LangChain / RAG / Vector DBs · 강의

RAG의 최신 동향 및 연구

검색 증강 생성과 LLM 통합 분야의 최신 발전, 연구 논문 및 향후 방향을 파악합니다.

RAG의 최신 동향 및 연구은(는) CoddyKit의 무료 LangChain / RAG / Vector DBs 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 LangChain / RAG / Vector DBs 학습 경로의 일부이며, 진행 상황이 웹과 CoddyKit 앱에 동기화됩니다. LangChain / RAG / Vector DBs 강의에는 총 4개의 강의가 포함되어 있습니다.

이 강의의 일부는 아직 번역되지 않았으며 영어로 표시됩니다.

Beyond Basic RAG

RAG is evolving fast! We've covered the basics, but researchers are constantly pushing boundaries. This lesson explores exciting new trends, from self-correcting models to multi-modal data.

Self-Correction & Self-RAG

A major trend is enabling LLMs to critique and improve their own work. Self-correction means the LLM can identify flaws in its generated answer or retrieved documents and try again.

Self-RAG is a framework where the LLM decides when to retrieve, generates an answer, and then critically evaluates both the retrieved info and its own response.
It can trigger further retrieval or regeneration steps if confidence is low.

Self-RAG in Action (Concept)

Imagine a loop where the LLM checks its own work. This conceptual Python example shows the core idea. We use mock components for demonstration purposes.

class MockRetriever:
    def retrieve(self, query):
        print(f"MockRetriever: Retrieving for '{query}'")
        return [f"Doc for {query} (initial)", f"Another doc for {query}"]

class MockLLM:
    def generate(self, query, docs):
        print(f"MockLLM: Generating for '{query}' with {len(docs)} docs.")
        return f"Generated response for '{query}' based on {len(docs)} docs."

    def critique(self, query, response, docs):
        print(f"MockLLM: Critiquing response: '{response}'")
        # Simulate a critique - for demo, always needs improvement first time
        if "initial" in response:
            return {"needs_improvement": True, "reason": "Initial docs might be too broad."}
        return {"needs_improvement": False}

    def refine_query(self, original_query, critique):
        print(f"MockLLM: Refining query based on critique: '{critique['reason']}'")
        return f"refined {original_query}"

def self_rag_process(query, retriever, llm):
    print(f"\n--- Starting Self-RAG for: '{query}' ---")
    initial_docs = retriever.retrieve(query)
    initial_response = llm.generate(query, initial_docs)

    critique = llm.critique(query, initial_response, initial_docs)

    if critique.get("needs_improvement"):
        print("Critique: Needs improvement. Refining...")
        new_query = llm.refine_query(query, critique)
        more_docs = retriever.retrieve(new_query)
        final_response = llm.generate(query, initial_docs + more_docs)
    else:
        print("Critique: No improvement needed.")
        final_response = initial_response
    print(f"--- Final Response: {final_response} ---\n")
    return final_response

if __name__ == "__main__":
    retriever = MockRetriever()
    llm = MockLLM()
    self_rag_process("What is the capital of France?", retriever, llm)

RAG Beyond Text: Multi-Modal

Traditional RAG focuses on text, but the world isn't just text! Multi-modal RAG extends retrieval to other data types like images, audio, or video.

Imagine querying about a product image and getting text descriptions, reviews, and related images.
It involves generating embeddings for different modalities and storing them in a shared vector space for unified search.

RAG with Knowledge Graphs

Sometimes, raw text isn't enough for precise factual answers. Knowledge Graph RAG combines the strengths of LLMs with structured knowledge graphs.

Knowledge graphs represent entities and their relationships (e.g., "Paris IS_CAPITAL_OF France").
RAG can retrieve relevant graph nodes/triples, then use an LLM to reason over this structured data, leading to more accurate and verifiable responses.

Adaptive & Dynamic RAG

Not all queries are created equal. Adaptive RAG systems can dynamically adjust their retrieval strategy based on the query or context.

For simple queries, a quick, broad search might suffice. For complex, nuanced questions, a multi-stage or deeper retrieval might be triggered.
Dynamic chunking is another aspect, where documents are split into chunks of varying sizes or based on semantic boundaries during retrieval, not just pre-processing.

RAFT: Fine-Tuning with Retrieval

We often fine-tune LLMs on specific tasks. Retrieval-Augmented Fine-Tuning (RAFT) integrates retrieval directly into this training process.

Instead of just training on static examples, RAFT teaches the LLM to read and utilize retrieved documents during its fine-tuning.
This helps the model learn how to better incorporate external knowledge, reducing reliance on memorized facts and improving its ability to handle new information.

New Metrics for Advanced RAG

Evaluating a basic RAG system is challenging enough! With these advanced techniques, evaluation becomes even more complex.

We need metrics that assess not just factual accuracy, but also the system's ability to self-correct, its multi-modal understanding, or its reasoning over knowledge graphs.
New benchmarks are emerging to specifically test these advanced RAG capabilities, focusing on reasoning, robustness, and adaptability.

Ethics & The Future of RAG

As RAG systems grow more sophisticated, so do their ethical implications. We must consider:

Bias amplification: Ensuring retrieved data doesn't introduce or amplify harmful biases.
Transparency: Making it clear why certain information was retrieved and used.
Data provenance: Tracking the origin and trustworthiness of all retrieved documents.

The future promises even more intelligent, adaptive, and integrated RAG systems across all domains.

Quick Check: RAG Evolution

Which of the following are considered emerging trends or advanced techniques in Retrieval Augmented Generation (RAG)?

Recap: RAG's Exciting Future

We've journeyed through the cutting edge of RAG! You learned about:

Self-correction and Self-RAG for autonomous improvement.
Multi-modal RAG for handling diverse data types.
Knowledge Graph RAG for enhanced factual accuracy.
Adaptive RAG and RAFT for smarter, more integrated systems.

The field is dynamic, promising more intelligent and context-aware AI applications!

자주 묻는 질문

“RAG의 최신 동향 및 연구” 강의는 무료인가요?

네 — “RAG의 최신 동향 및 연구” 전체 내용을 이 웹사이트에서 무료로 읽을 수 있습니다. 인터랙티브하게 실습하려면(내장 코드 에디터와 24/7 AI 튜터), CoddyKit PRO로 업그레이드하면 LangChain / RAG / Vector DBs 강의 전체를 잠금 해제할 수 있습니다. LangChain / RAG / Vector DBs 강의에는 총 4개의 강의가 포함되어 있습니다.

“RAG의 최신 동향 및 연구”에서 뭘 배우나요?

검색 증강 생성과 LLM 통합 분야의 최신 발전, 연구 논문 및 향후 방향을 파악합니다. 브라우저에서 직접 실행하는 실습 코드로 LangChain / RAG / Vector DBs을(를) 배우며, 24/7 AI 튜터가 강의를 진행하면서 질문에 답변해줍니다.

LangChain / RAG / Vector DBs을(를) 시작하는 데 경험이 필요한가요?

사전 경험은 필요하지 않습니다. CoddyKit의 LangChain / RAG / Vector DBs은(는) 초급자부터 고급 학습자까지를 위해 구성되어 있으므로, 여기서 시작하거나 처음부터 시작할 수 있으며 자신의 속도대로 진행할 수 있습니다. 이것은 4개 중 3번째 강의입니다.

“RAG의 최신 동향 및 연구” 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 LangChain / RAG / Vector DBs 강의에서 코드를 작성하고 실행할 수 있나요?

네. 모든 LangChain / RAG / Vector DBs 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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