🔍 RAG: Retrieval-Augmented Generation

📚 Phase 1: Data Indexing & Preparation
1
📄 Document Collection
Gather documents, PDFs, web pages, databases
"AI Research Papers" + "Company Docs" + "FAQ Database"
2
✂️ Text Chunking
Split documents into smaller, manageable pieces
Chunk Size: 512-1024 tokens
Overlap: 50-100 tokens
3
🧮 Generate Embeddings
Convert text chunks to vector representations using models like Sentence-BERT
[0.12, -0.34, 0.67, ...]
[0.89, 0.23, -0.45, ...]
4
🗄️ Store in Vector DB
Index embeddings in vector database for fast similarity search
Vector Databases:
FAISS • Pinecone • Weaviate • ChromaDB • Qdrant
⬇️
❓ Phase 2: Query Processing & Retrieval
5
💬 User Query
User asks a question
Example:
"What are the latest trends in AI research?"
6
🔢 Query Embedding
Convert user query to same vector space as documents
Query → [0.45, -0.12, 0.78, ...]
7
🎯 Similarity Search
Find most relevant chunks using cosine similarity
Doc A: 0.87
Doc B: 0.82
Doc C: 0.79
8
📋 Retrieve Top-K
Select most relevant documents (typically 3-5)
Retrieved: Top 5 most similar chunks
Similarity threshold: > 0.75
⬇️
🤖 Phase 3: Context Augmentation & Generation
9
📝 Context Assembly
Combine retrieved documents with user query in prompt
"Based on the following context: [Retrieved Docs]
Answer the question: [User Query]"
10
🧠 LLM Generation
Large Language Model generates response using provided context
GPT-4 • Claude • Llama • Gemini
11
✨ Final Response
Contextually-aware, accurate answer delivered to user
Response:
"Based on recent research, the latest AI trends include..."
Query → Context → Response

⚠️ Key Challenges & Considerations

🔪 Chunking Strategy
Challenge: How to split documents effectively?
Solutions:
  • Sentence-based chunking
  • Paragraph-based chunking
  • Semantic chunking
  • Overlapping windows
🎯 Relevance & Quality
Challenge: Ensuring retrieved content is truly relevant
Solutions:
  • Fine-tune embedding models
  • Hybrid search (semantic + keyword)
  • Re-ranking models
  • Metadata filtering
📏 Context Length Limits
Challenge: LLM token limits constrain context size
Solutions:
  • Smart chunking strategies
  • Context compression
  • Hierarchical retrieval
  • Multi-turn conversations
⚡ Performance & Scale
Challenge: Fast retrieval at scale
Solutions:
  • Approximate nearest neighbors (ANN)
  • Efficient vector indexing
  • Caching strategies
  • Distributed vector stores