Choosing Between Fine-Tuning and RAG

Summary

A recurring real-world design decision — fine-tuning bakes in behavior/style, RAG grounds answers in up-to-date external knowledge, and many systems genuinely need both.

How to Learn This

1Build a decision framework: use RAG for facts/freshness, fine-tuning for style/format/behavior.
2Practice justifying this choice for 2-3 different hypothetical products.
3Learn how combining both (fine-tuned model + RAG) is common in production systems.

Handling High-Throughput Inference

Arrays & Strings

More topics in System Design for AI Applications

Designing Scalable AI Systems Latency & Cost Optimization Caching LLM Responses Load Balancing AI Workloads Designing a RAG System at Scale Designing a Recommendation System Designing a Content Moderation System Handling High-Throughput Inference

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