What is a RAG?

RAG = Query + Prompt + Context + LLM

Curiosity: What is a RAG? How can we retrieve knowledge from external sources and augment LLM responses to create more accurate, context-aware answers?

Retrieval-Augmented Generation (RAG) is a powerful technique that combines the best of information retrieval and language generation. This simple equation captures the essence of RAG: Query + Prompt + Context + LLM = Enhanced Response.

Understanding the RAG Equation

graph TB
    A[User Query] --> B[Query Processing]
    B --> C[Vector Search]
    C --> D[Retrieve Relevant Context]
    D --> E[Augment Prompt]
    E --> F[LLM Generation]
    F --> G[Enhanced Response]
    
    H[Knowledge Base] --> C
    I[Embedding Model] --> C
    
    style A fill:#e1f5ff
    style D fill:#fff3cd
    style F fill:#d4edda
    style G fill:#f8d7da

RAG Components Breakdown

Component	Purpose	Key Technologies
Query	User’s information need	Natural language processing, query expansion
Prompt	Instructions for LLM	System prompts, few-shot examples, templates
Context	Retrieved relevant information	Vector databases, embedding models, similarity search
LLM	Text generation	GPT-4, Claude, Llama, Mistral

Core RAG Workflow

The RAG process consists of several critical steps:

1. Indexing Phase (Offline)

from langchain.text_splitter import RecursiveCharacterTextSplitter
from langchain.embeddings import OpenAIEmbeddings
from langchain.vectorstores import Chroma

# Step 1: Load documents
documents = load_documents("knowledge_base/")

# Step 2: Split into chunks
text_splitter = RecursiveCharacterTextSplitter(
    chunk_size=1000,
    chunk_overlap=200
)
chunks = text_splitter.split_documents(documents)

# Step 3: Create embeddings
embeddings = OpenAIEmbeddings()
vectorstore = Chroma.from_documents(
    documents=chunks,
    embedding=embeddings
)

2. Retrieval Phase (Online)

# Step 4: Retrieve relevant context
query = "What is RAG?"
retriever = vectorstore.as_retriever(search_kwargs={"k": 3})
relevant_docs = retriever.get_relevant_documents(query)

3. Augmentation Phase

# Step 5: Augment prompt with context
context = "\n\n".join([doc.page_content for doc in relevant_docs])
augmented_prompt = f"""Use the following context to answer the question.

Context:
{context}

Question: {query}

Answer:"""

4. Generation Phase

# Step 6: Generate response
from langchain.llms import OpenAI

llm = OpenAI(temperature=0)
response = llm(augmented_prompt)
print(response)

Key Concepts in RAG

Indexing

• Purpose: Prepare knowledge base for efficient retrieval
• Process: Document loading → Chunking → Embedding → Storage
• Tools: LangChain, LlamaIndex, Haystack

Loading

• Sources: PDFs, web pages, databases, APIs
• Formats: Text, markdown, structured data
• Considerations: Data quality, format consistency

Splitting

• Strategies: Character-based, sentence-based, semantic
• Chunk Size: Balance between context and precision (typically 200-1000 tokens)
• Overlap: Maintain context continuity (typically 10-20% overlap)

Embedding

• Models: OpenAI

  text-embedding-ada-002
  

  , Sentence Transformers, Cohere
• Dimensions: 384, 768, 1536 (model-dependent)
• Evaluation: MTEB Leaderboard, domain-specific benchmarks

Storing

• Vector Databases: Pinecone, Weaviate, Chroma, Qdrant, SingleStore
• Features: Similarity search, filtering, metadata storage
• Scalability: Horizontal scaling, distributed search

Retrieval

• Methods: Dense retrieval, sparse retrieval, hybrid search
• Metrics: Cosine similarity, dot product, Euclidean distance
• Optimization: Re-ranking, query expansion, multi-query

Augmentation

• Techniques: Context injection, prompt engineering, few-shot examples
• Best Practices: Clear separation of context and query, structured prompts

Generation

• Models: GPT-4, Claude, Llama, Mistral
• Parameters: Temperature, max_tokens, top_p
• Optimization: Streaming, caching, parallel generation

Advanced RAG Techniques

Multi-Query Retrieval

from langchain.retrievers.multi_query import MultiQueryRetriever

# Generate multiple query variations
retriever = MultiQueryRetriever.from_llm(
    retriever=vectorstore.as_retriever(),
    llm=OpenAI()
)

# Retrieve diverse perspectives
docs = retriever.get_relevant_documents(query)

Contextual Compression

from langchain.retrievers import ContextualCompressionRetriever
from langchain.retrievers.document_compressors import LLMChainExtractor

# Compress retrieved documents to most relevant parts
compressor = LLMChainExtractor.from_llm(OpenAI())
compression_retriever = ContextualCompressionRetriever(
    base_compressor=compressor,
    base_retriever=vectorstore.as_retriever()
)

RAG vs. Fine-tuning Comparison

Aspect	RAG	Fine-tuning
Knowledge Update	✅ Real-time	❌ Requires retraining
Source Attribution	✅ Yes	❌ No
Cost	💰 Lower (API calls)	💰💰 Higher (training)
Domain Adaptation	⚠️ Limited	✅ Strong
Hallucination	⚠️ Possible	⚠️ Possible
Best For	Dynamic knowledge, citations	Style, format, domain expertise

Tools and Resources

Tool	Purpose	Link
SingleStore	Vector database with SQL interface	SingleStore
LlamaIndex	Data framework for LLM applications	LlamaIndex
Superlinked	Vector database API	Superlinked
MTEB Leaderboard	Embedding model benchmarks	Hugging Face MTEB
LangChain	LLM application framework	LangChain

Webinar Information

On May 16th, Thursday, I gave a webinar covering these concepts in depth:

Hosts: Matt Brown, Akmal Chaudhri, Esq.

The webinar covered:

• Mathematical foundations of RAG
• Practical implementation examples
• Advanced techniques and optimizations
• Real-world use cases and best practices

RSVP: https://by-hand.ai/rag

Key Takeaways

Retrieve: RAG enables LLMs to access up-to-date, domain-specific information without retraining.

Innovate: By combining retrieval and generation, we can build systems that are both knowledgeable and flexible.

Curiosity → Retrieve → Innovation: The RAG workflow embodies this cycle—curiosity drives the query, retrieval brings relevant knowledge, and innovation emerges from the augmented generation.

Translate to Korean

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RAG = 쿼리 + 프롬프트 + 컨텍스트 + LLM ~ RSVP 👉 https://by-hand.ai/rag

RAG란 무엇입니까? 이것은 RAG가 무엇인지에 대한 이해를 개념화하기 위해 생각해 낸 간단한 방정식입니다.

5월 16일 목요일에는 웨비나를 진행합니다.

RAG 초보자 가이드

제 RAG 방정식에 대한 설명을 듣고 싶으시면 웨비나에 참여하십시오. RSVP 링크는 위에 있습니다.

해시태그#rag 해시태그#vectordatabases 해시태그#aibyhand

–개념–

RAG 방정식과 관련된 주요 개념을 다루겠습니다.

•인덱싱 •로드 •분할 •포함 •저장 •검색 •확대 •세대

시간이 허락한다면 다음과 같은 고급 기술에 대해 이야기 할 수 있습니다.

• 다중 쿼리 • 상황별 압축

–수학–

항상 그렇듯이 제 웨비나에서 수학을 기대해야 합니다. 그러나 수학은 재미있고, 접근하기 쉬우며, 유용할 것이라고 장담합니다.

–도구–

다음과 같은 다양한 유용한 도구에 대한 예를 포함하겠습니다. SingleStore LlamaIndex Superlinked 의 API Hugging Face 의 MTEB 리더보드