Three techniques to adapt LLMs for any use case

This post introduces three techniques for adapting LLMs: prompt engineering (zero-shot, few-shot, and explicit instructions), retrieval-augmented generation (RAG) using vector databases for context, and fine-tuning to modify model behavior. It details the use cases, costs, and trade-offs of each method, guiding users to start with prompt engineering and progress to RAG or fine-tuning as needed. The post emphasizes that prompt engineering is low-cost and effective for many tasks, RAG offers moderate cost and unlocks data-aware use cases, and fine-tuning has a high upfront cost but low ongoing cost for customized behavior.