Search⌘ K
AI Features
Home
Courses
Ace the AI Engineer Interviews

Few-Shot Learning

Explore few-shot prompting to understand how large language models adapt to new tasks using a handful of examples without updating weights. Learn to distinguish it from zero-shot and fine-tuning approaches, construct effective prompts, and recognize its practical benefits and limitations. This lesson equips you to confidently explain and apply few-shot prompting in AI interviews and real-world NLP tasks.

We'll cover the following...

Few-shot prompting has emerged as a crucial skill for effectively leveraging large language models without requiring fine-tuning. In interviews, especially at the junior level, you’re often asked to explain what few-shot prompting is, how it works, and how it compares to zero-shot or one-shot prompting. These questions help interviewers assess your familiarity with prompt engineering, often ignored, yet a foundational concept in modern AI system design.

Why is this such a popular interview topic? Because few-shot prompting enables in-context learning: a pretrained LLM can mimic task behavior by observing just a handful of examples embedded in a prompt. This bypasses the need for additional gradient-based training. The ability to steer a model toward the desired behavior with only a few well-crafted examples is a practical superpower in today’s LLM workflows—used everywhere from prototype chatbots to commercial agents. Interviewers use this question to confirm that you understand this new paradigm of using LLMs as programmable tools through prompts, not just training data.

In this breakdown, we’ll walk through the key aspects an interviewer expects:

  • Why few-shot prompting emerged as a solution to improve performance on complex tasks where zero-shot models underperform—by showing the model a few examples within the prompt to steer its behavior without retraining;

  • How few-shot prompting enables in-context learning, allowing large language models to generalize task behavior from a small set of examples included in the input, without updating the model’s weights.

  • How to distinguish few-shot prompting from zero-shot and one-shot approaches, and when to use each, depending on the task complexity, ambiguity, or format sensitivity.

  • How to construct effective few-shot prompts by choosing representative examples, maintaining consistent formatting, and being aware of context window constraints.

  • Where few-shot prompting excels and where it falls short, including its strengths in flexible, low-data scenarios and limitations in reasoning-heavy or memory-intensive tasks.

Interview trap: An interviewer might ask, “Is few-shot prompting the same as few-shot learning in traditional ML?” and candidates sometimes say, “Yes, they’re the same concept.”

However, they’re quite different! Traditional few-shot learning (meta-learning) involves training a model to quickly adapt to new tasks with a limited number of examples—the model's weights are updated. In LLM few-shot prompting, there’s no weight update at all. The examples are just part of the input, and the model uses its attention mechanism to condition on them. The “learning” happens purely through the forward pass, not gradient descent. This distinction is important because it explains why few-shot prompting is instant (requiring no training) but also temporary (the knowledge it provides doesn’t persist beyond the prompt).

Strong candidates also articulate trade-offs, such as where few-shot prompting works well (e.g., tasks with consistent structure) and where it might fall short (e.g., tasks requiring deep reasoning or long-term memory). Mentioning model behavior patterns—like the importance of formatting consistency, the effect of recency in examples, or the limits of context window length—can elevate your answer.

What is few-shot prompting, and how does it compare to zero-shot and one-shot?

Few-shot prompting is a technique where you provide a language model with a handful of input-output examples inside your prompt. This way, instead of going in “cold” (zero-shot), the model sees what you want by example. Think of it like tutoring: show a student two solved math problems, then give them a third, in which they’ll likely mimic the pattern.

Educative byte: The term “few-shot learning” was popularized by the GPT-3 paper (Brown et al., 2020), which demonstrated that scaling language models enabled remarkable in-context learning. The paper showed GPT-3 could perform tasks it was never explicitly trained for by simply providing examples in the prompt. This was a paradigm shift—previously, adapting models to new tasks required fine-tuning with hundreds or thousands of examples. GPT-3 showed that sufficiently large models could “learn” from just a few examples at inference time.

In interviews, it’s good to mention the spectrum of prompting styles:

  • Zero-shot prompting: This means you directly ask the model to perform a task with no examples provided. For instance, you just instruct, “Translate the following sentence to French: I am happy.” The model must determine what you want (translation, in this case) from the instruction and its general knowledge. Large LLMs can do zero-shot tasks because their training data likely included similar tasks, or they can infer what is being asked. However, zero-shot performance might be poor for more complex or unclear tasks because the model isn’t sure what format or style of answer is expected. As noted by researchers, while LLMs demonstrate remarkable zero-shot capabilities, they often fall short in more complex tasks.

Zero-shot prompting
Zero-shot prompting

  • One-shot prompting: This is the intermediate case where you give one example before the query. It’s a special case of few-shot prompting (in fact, ...