Choosing Between RAG, ICL, and Fine-Tuning In LLMs
Understand how to evaluate and choose between RAG, in-context learning, and fine-tuning techniques to improve large language model performance. Explore their pros and cons in real-world scenarios and learn to justify your decisions based on cost, accuracy, data sensitivity, and update frequency.
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, and how do they differ fundamentally?
Before we discuss comparisons, edge cases, and real-world scenarios, you should demonstrate to the interviewer that you can establish a clear and concise framework with well-defined terms.
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:
Q: What’s 2+2?A: 4Q: What’s 3+3?A:
The model decides to continue the pattern. This is why ICL is often referred to as “few-shot” or “zero-shot” learning, depending on ...