Choosing Between RAG, ICL, and Fine-Tuning In LLMs

In many interviews for AIML roles, you’ll be asked to compare different methods of getting a language model to perform better on specific tasks or new information. The question often compares retrieval-augmented generation (RAG) with in-context learning (ICL), and sometimes touches on fine-tuning. It’s designed to test whether you understand the various ways to enhance an LLM’s performance and knowledge, and the pros/cons of each approach.

The engineer should know why and when to choose one over the other. Interviewers want engineers who can reason across performance, cost, memory, and model reliability when the underlying weights are fixed, and yet the system still needs to improve or adapt to new information.

What are ICL, RAG, and fine-tuning?

Before we discuss comparisons, edge cases, and real-world scenarios, you should show the interviewer that you can set the stage with clear, concise definitions.

In-context learning (ICL) refers to teaching the model a task at inference time by including examples in the prompt. Think of it like showing the model how to do the task whenever you call it. You don’t change the model’s weights; instead, you craft a prompt like:

The model decides to continue the pattern. This is why ICL is often called “few-shot” or “zero-shot” learning, depending on whether you include examples or just instructions. ...