Fine-Tune vs. Prompt vs. RAG
Explore how to diagnose failures in large language model applications and match solutions like prompting, fine-tuning, or retrieval-augmented generation to specific issues such as format drift, knowledge updates, and consistency. Understand how latency, cost, data needs, and privacy impact these choices to build reliable production AI systems.
A support assistant must answer policy questions in a consistent house style, return valid JSON, meet a tight latency budget, and cite a policy corpus that changes daily. The same request can expose different failures depending on how the system is designed.
With prompting alone, the assistant may return the expected JSON keys while varying its tone or including inconsistent optional fields. The response may parse successfully but violate the application’s schema or formatting expectations, even when its factual content is correct.
Adding retrieval gives the assistant access to current policy text, provided the index is updated. It does not automatically stabilize tone or enforce JSON structure. Retrieval may also miss relevant passages, and generation may produce claims unsupported by the retrieved evidence.
Fine-tuning can improve consistency in structure and house style. However, policies learned ...