RAG Systems at Scale: Architecture Patterns for Knowledge-Driven AI

The Problem: LLMs Don't Know Your Data

LLMs are trained on public internet data. They don't know:

• Your company policies
• Your internal documentation
• Your custom data formats
• Recent updates (knowledge cutoff)

So they hallucinate. They make things up. Users ask: "What's our refund policy?" LLM makes up a policy.

Solution: RAG (Retrieval-Augmented Generation). Retrieve relevant documents from YOUR knowledge base, then have the LLM answer based on that.

What Is RAG (Really)?

RAG = Retrieval + Generation.

1. Retrieval: Find documents relevant to the user's query
2. Generation: LLM reads those documents and answers the query

Simple example:

RAG Architecture (4 Components)

1. Knowledge Base (Documents)

Your data. Could be:

• PDFs, Word docs
• Internal wikis, Notion pages
• Database records
• Customer interactions

2. Embeddings (Vector Representations)

Convert documents into vectors (numbers). Semantic similarity = close vectors.

3. Vector Database (Search Index)

Store embeddings. When user queries → Search for similar embeddings → Retrieve documents.

Examples: Pinecone, Weaviate, Milvus

4. LLM (Generation)

Read retrieved documents + original query → Generate answer.

Indexing Strategy (How to Chunk)

You can't index 1000-page PDFs as single documents. Too large. Need to chunk them.

Strategy 1: Fixed-Size Chunks

Split into 512-token chunks. Simple, but loses context.

Strategy 2: Semantic Chunks

Split by topic/paragraph. Preserves meaning.

Strategy 3: Hierarchical Chunks

Chunks within chunks. Document > Sections > Paragraphs. Retrieve at appropriate level.

Strategy	Speed	Accuracy	Best For
Fixed-Size	⚡⚡⚡	⭐⭐	Quick POCs
Semantic	⚡⚡	⭐⭐⭐	Production
Hierarchical	⚡	⭐⭐⭐⭐	Enterprise

Retrieval Optimization

Problem: Irrelevant Results

Sometimes search returns irrelevant documents. LLM tries to answer based on wrong docs.

Solution 1: Hybrid Search

Combine semantic search + keyword search.

• Semantic: Find conceptually similar docs
• Keyword: Find docs with exact terms
• Combine: Get best of both

Solution 2: Reranking

Retrieve 50 documents. Rerank by relevance. Use only top 5 for LLM.

Solution 3: Query Expansion

User asks: "Refund process?"
Expand to: "Returns, refunds, money-back guarantee, reimbursement"
Search for all variations

Scaling RAG to Enterprise

Consideration 1: Document Updates

Policies change. How do you update embeddings?

• Real-time: Update on publish
• Batch: Daily/weekly refresh
• Hybrid: Hot documents real-time, others batch

Consideration 2: Latency

Retrieval + Generation takes time:

• Search vector DB: 100-500ms
• LLM generation: 1-5 seconds
• Total: 2-6 seconds (acceptable for chat)

Consideration 3: Cost

Each query involves:

• Embeddings API call (for query)
• Vector DB search
• LLM generation

Cost per query: ~$0.005-0.01 at scale

Key Takeaways