AI/ML, Cloud Computing, Data Analytics

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The Power of Zero-Shot and Few-Shot Prompting in Large Language Models


The emergence of Large Language Models (LLMs) has ignited significant interest within the AI community. Various powerful generative AI solutions, such as GPT 3.5, GPT 4, and BARD, have captured our attention. Amazon Bedrock houses multiple LLM models tailored for diverse use cases, ranging from question answering to creative text generation and critical analysis.

We aim to explore how we can utilize Large Language Models to achieve specific goals or obtain desired responses through various prompting techniques. We will critically examine the advantages and drawbacks of these methods to gain a deeper understanding of their effectiveness.

Before diving into the prompting methods, we must understand what is prompting.

What is Prompting?

Let us begin by defining Large Language Models (LLMs). An LLM, short for Large Language Model, is a sophisticated deep learning architecture comprising numerous layers of transformers and feed-forward neural networks containing hundreds of millions to billions of parameters.

These models undergo training on extensive datasets from various origins and are designed to comprehend and produce text. Some notable applications include language translation, text summarization, question answering, content generation, and beyond.

Giving a task to a Generative LLM generates the relevant text based on the input provided. But how do we instruct a Generative LLM to perform a specific task? The process is straightforward: we furnish it with written instructions. LLMs are engineered to interpret and respond to users’ requests according to these instructions, also known as prompts.

A prompt serves as guidance to a Large Language Model (LLM). You’ve provided prompts if you’ve engaged with an LLM such as ChatGPT. The goal of a prompt is to return a response that is accurate, well-formed, and of appropriate length.

In essence, prompting involves formulating your intention into a query using natural language, aiming to evoke the desired response from the model.

Therefore, from the above discussion, we can conclude that prompting is used to get the response from the LLM model, and to get a relevant, accurate response, one can understand the importance of the prompting.


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Zero-Shot Prompting

Direct prompting, also called Zero-shot, represents the most straightforward form of prompting. It involves providing the model with instructions only, without any accompanying examples.

Under zero-shot prompting, language models demonstrate the ability to perform tasks without explicit training on the specific task at hand. Instead, these models rely on their pre-trained knowledge and understanding of language to generate relevant outputs based on provided prompts or instructions. For example, a zero-shot prompt might instruct a language model to summarize a given text without prior training on summarization tasks. By leveraging its understanding of language structures and patterns encoded during pre-training, the model can generate coherent summaries that capture the essence of the input text.

To comprehend the utility of this approach, consider the scenario of sentiment analysis: Traditionally, you would annotate paragraphs with sentiment labels and train a machine learning model (such as a Recurrent Neural Network on text data) to input paragraphs and output sentiment classifications. However, this model proves inflexible; introducing a new classification category or requesting summarization instead of classification necessitates modification and retraining.

In contrast, a large language model does not require retraining. Formulating the correct prompt can instruct the model to classify or summarize a paragraph. While the model may struggle to classify paragraphs into ambiguous categories like “A” or “B,” it can effectively differentiate between “positive sentiment” and “negative sentiment” since it comprehends the meaning of these terms. This capability stems from the model’s training, which acquired an understanding of such words and the ability to execute straightforward instructions.


Here, we provide a direct prompt to the LLM model as an input without providing any training on the use case we want it to solve, and we get the response from the model.


Classify the text into neutral, negative, or positive.

Text Input: I think the boat looks just okay.

Sentiment (Output of LLM model): Neutral.

Few-Shot Prompting

Although large-language models excel in zero-shot tasks, they struggle with more complex tasks in this mode. We can employ few-shot prompting to improve performance, including examples in the prompt to guide the model. These examples help the model better understand subsequent tasks and generate more accurate responses.

Unlike zero-shot prompting, few-shot prompting entails including a small number of labeled examples in the prompt. This distinguishes it from traditional few-shot learning, where the LLM undergoes fine-tuning with a few samples for a novel problem. Such an approach reduces dependency on extensive labeled datasets by enabling models to quickly adapt and generate accurate predictions for new classes using only a few labeled samples. This method proves advantageous when acquiring a substantial amount of labeled data for new classes, which is time-consuming and labor-intensive.

If you’re having trouble explaining what you need but still want a language model to provide answers, you can give it some examples, and this will get much clearer with an example we will discuss below.


This is the sample instruction provided to the model, and then we will pass a test case to check if the LLM model provides a result in the desired format.

Text: The food at the restaurant was delicious.

Classification: Pos

Text: My new phone has a sleek design.

Classification: Neu

Text: I received excellent customer service at the store.

Classification: Pos


Text: The traffic jam made me late for work again.

Output from LLM:

Classification: Neg


Prompting methods serve as a connection between Large Language Models (LLMs) and various tasks. While zero-shot prompting, which doesn’t rely on pre-existing labeled data, demonstrates the models’ ability to generalize and tackle new challenges, it often falls short of achieving comparable or superior performance compared to fine-tuning. On the other hand, few-shot prompting emerges as a promising alternative to fine-tuning, as demonstrated by numerous examples and benchmark comparisons. By incorporating a small number of labeled examples within prompts, few-shot prompting shows potential across a spectrum of tasks.

Drop a query if you have any questions regarding Prompting methods and we will get back to you quickly.

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1. What is one-shot prompting?

ANS: – One-shot prompting involves providing a single example or instance to a language model to perform a task. The model can generalize from the given example and generate responses accordingly.

2. How does few-shot prompting differ from one-shot prompting?

ANS: – Few-shot prompting expands on one-shot prompting by providing a small number of examples in the prompt. This allows the model to better adapt to the task and improve its performance with limited training data.

3. Are there any limitations to one-shot and few-shot prompting?

ANS: – While one-shot and few-shot prompting can achieve impressive results with minimal data, they may struggle with highly complex or nuanced tasks that require extensive training or domain-specific knowledge. Also, the effectiveness of these techniques may vary depending on the quality and diversity of the examples provided.

WRITTEN BY Parth Sharma



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