Large Language Model (LLM) system design

🧠Key Takeaways

1
RAG (Retrieval-Augmented Generation): retrieve relevant context, inject into prompt → reduces hallucinations
2
LLM inference is GPU-bound and expensive — prompt caching, batching, and model quantization reduce cost
3
Prompt engineering is system design: structured prompts, chain-of-thought, output schemas (JSON mode)
4
Guardrails: content filtering, output validation, token limits, rate limiting — essential for production

Building Systems with LLMs

LLM system design is the most important new skill for system designers in 2025+. The challenge isn't training LLMs — it's building reliable, cost-effective, safe production systems around them: RAG pipelines, prompt management, result validation, and scaling inference.

Document Ingestion

Chunk documents into segments (500-1000 tokens). Embed each chunk using an embedding model (text-embedding-3-small). Store embeddings in a vector database (Pinecone, Weaviate, pgvector).

✅Cost Optimization

LLM inference cost is dominated by prompt tokens. Strategies: (1) Prompt caching — reuse prefilled KV cache for common system prompts (60-90% cost reduction). (2) Smaller models for classification/routing, large models for generation. (3) Batch requests during off-peak. (4) Quantized models (INT8/INT4) for 2-4x cost reduction with minimal quality loss.

Advantages

•RAG enables LLMs to answer domain-specific questions accurately
•Prompt caching dramatically reduces inference cost
•Structured output (JSON mode) enables reliable integrations

Disadvantages

•LLM inference is expensive and GPU-constrained
•Hallucinations can never be fully eliminated
•Prompt engineering is more art than science

🧪 Test Your Understanding

Knowledge Check1/1

What does RAG (Retrieval-Augmented Generation) primarily solve?