Large language models (LLMs) have transformed how businesses generate content, automate tasks, and handle complex data. However, the effectiveness of these models heavily depends on how well they’re prompted.
One key aspect of prompting is understanding zero-shot, one-shot, and few-shot prompting. These approaches determine how much context or example data the model needs to generate a desired output. You will find, in many cases that the time taken to develop an effective few-shot prompt is time well spent, if the use case is important!
In this blog, we’ll break down what these terms mean and how they can be applied to business use cases.
What is Zero-Shot Prompting?
Zero-shot prompting is when you ask the model to perform a task without providing any examples. You simply describe the task in the prompt, and the model uses its general knowledge to generate a response. This approach works best for straightforward tasks or general knowledge queries.
Example Task: Write a product description for a luxury handbag.
Zero-shot Prompt:
Write a product description for a luxury handbag that highlights its quality craftsmanship and premium materials.Output:
This luxury handbag, crafted from the finest leather, features elegant stitching and durable construction. Its timeless design makes it a perfect accessory for any occasion.Use Case:
Zero-shot prompting can be used in business for basic content generation like product descriptions, summarising reports, or drafting emails. It's fast and effective for tasks where the model already understands the domain.
Limitations:
While zero-shot prompting is convenient, it may not always generate highly accurate or specialised responses, especially for more complex or industry-specific tasks.
What is One-Shot Prompting?
One-shot prompting involves providing the model with a single example of the desired output to guide its response. It is often helpful to provide an example prompt, or question which you would have given to the model to create that output.
By offering a specific format or context, the model gains a better understanding of your expectations, which improves the accuracy of its output.
Example Task: Draft an email requesting a meeting.
One-shot Prompt:
Example Prompt: ```Request a meeting with John```
Example Answer: ```Hi John, I hope you’re well. I wanted to arrange a meeting to discuss the upcoming project. Please Let me know your availability.```
Now, write a similar email to Jane requesting a meeting to discuss a different project.Output:
Hi Jane, I hope you’re doing well. I wanted to schedule a meeting to discuss the upcoming marketing campaign. Please let me know when you’re available.Use Case:
In business, one-shot prompting is useful for generating outputs that need to follow a specific structure or tone, such as emails, legal clauses, or summaries. The example helps the model better match your desired format.
Limitations:
Although more accurate than zero-shot prompting, one-shot prompting may still produce inconsistent results for highly specialised tasks. The model has limited information to fully understand complex requirements.
What is Few-Shot Prompting?
Few-shot prompting involves providing the model with several examples of the desired output. Similarly to one-shot prompting, optionally providing a sample input (sample prompt) can be helpful in directing the model to determine what is important from your input prompt/instruction.
This method allows the model to better understand the task and generate more precise and relevant responses, especially when handling complex or nuanced tasks.
Example Task: Generate a social media post for a new fitness class.
Few-shot Prompt:
Here are a few examples of social media posts for new fitness classes:
Example Prompt 1: ```Write a post for our spin class on saturday```
Example Answer 1: ```Join us for a high-energy spin class this Saturday! Burn calories and boost your mood with an intense cardio session.```
Example Prompt 2: ```Write a post for our yoga class```
Example Answer 2: ```Don’t miss our upcoming yoga session! Perfect for beginners, this class will help you unwind and find your inner peace. ```
Write a post for a new CrossFit class.Output:
Get ready to push your limits with our new CrossFit class! Build strength, endurance, and agility with a workout designed for all fitness levels. Sign up now!Use Case:
Few-shot prompting is highly effective for generating content that needs to match a specific tone or format, such as marketing copy, legal text, or technical documentation. The more examples you provide, the better the model’s output aligns with your expectations.
Limitations:
Few-shot prompting requires more setup time, as you need to provide multiple examples. It can also be less efficient for simpler tasks where examples aren’t needed. However, it is incredibly powerful for generating high-quality, consistent outputs.
Choosing the Right Approach for Your Business: Cost vs Output Quality
When it comes to choosing between zero-shot, one-shot, and few-shot prompting, it's important to consider both the quality of output and the costs associated with API usage—especially when using different language model providers like OpenAI, Perplexity, Gemini, or Anthropic. OpenAI has recently introduced 'prompt caching' in some it's models, which reduces the cost difference between zero and few-shot prompting by as much as 50%.
Each approach has its trade-offs in terms of accuracy, complexity, and the cost of running these models. When using the flagship online interface like chatgpt, this isn't such a concern as the usage is uncapped, but the value unlocked by automating processes, workflows and creation of outputs at scale is left on the table.
Zero-Shot Prompting: Low Cost, Lower Accuracy
Zero-shot prompting is the most cost-effective approach because it requires no examples, just the prompt itself. This method consumes less computational power, and since most APIs charge based on tokens (the number of words or characters processed), it tends to be cheaper.
Pros:
Lower cost: Fewer tokens are required, making it cost-efficient for simpler tasks.
Speed: Fast output generation, suitable for straightforward tasks like basic emails or quick answers.
Cons:
Lower accuracy: Without examples, the model might misinterpret complex tasks or give responses that require further refinement, leading to additional prompting, which can drive up costs indirectly.
For APIs like OpenAI’s GPT-4 or Anthropic's Claude, zero-shot prompting is great when you need quick, simple outputs. However, for tasks requiring precision or context, the cost savings might not justify the time spent on correcting or refining outputs.
One-Shot Prompting: Moderate Cost, Higher Accuracy
One-shot prompting strikes a balance between cost and output quality. By providing just one example, you improve the model’s understanding of the task, which can reduce the need for further clarification and retries.
Pros:
Better quality output: One example gives the model a clearer understanding of what’s needed, improving relevance and reducing errors.
Cost-effective for medium complexity tasks: Though it uses more tokens than zero-shot, it often results in more accurate outputs, reducing the need for additional iterations.
Cons:
Slightly higher cost: You’ll incur higher token usage compared to zero-shot, but this cost is often justified by the improvement in output quality.
APIs like OpenAI and Gemini (Google’s AI language model) tend to perform well in one-shot prompting for tasks such as generating marketing emails or customer service responses. The improved output quality can save time and lower costs in the long run by reducing the need for repeated prompts.
Few-Shot Prompting: Higher Cost, Best Accuracy
Few-shot prompting, where multiple examples are provided, typically results in the highest quality output but also incurs the highest cost in terms of token usage. This method excels in tasks requiring high precision or specific formatting, such as legal documentation, technical writing, or generating consistent responses for customer support. In most cases, 3-4 examples seems to be the 'sweet spot'.
Pros:
Best output quality: Few-shot prompting provides enough context for the model to understand nuanced tasks, ensuring consistency across responses.
Powerful for repetitive tasks: When the same task is repeated frequently (e.g., generating responses for customer service or summarising reports), few-shot prompting leads to more reliable and uniform results, minimising errors and saving time on post-editing.
Cons:
Higher cost: With more tokens used for examples, few-shot prompting can significantly increase API costs, particularly for models like GPT-4 or Anthropic’s Claude that have high token limits and associated costs.
However, for repetitive tasks where consistency and quality matter (e.g., generating customer service replies or drafting reports), the cost is often justified. A more accurate output reduces the need for manual corrections, and in the long run, it can streamline processes, making it worth the investment.
Smaller, cheaper models such as GPT-4o-mini and Gemini-flash models may perform better than larger models (using zero-shot) when prompted with examples - so this is worth considering when optimising workflows with many operations.
Conclusion:
Choosing between zero-shot, one-shot, and few-shot prompting depends largely on the task complexity and the trade-off between cost and quality.
Zero-shot prompting is ideal for quick, simple tasks but may lack accuracy for specialised needs.
One-shot prompting balances cost and quality for medium-complexity outputs.
Few-shot prompting, though more expensive, delivers the highest accuracy and consistency, particularly for repetitive, high-value business tasks like customer service, report generation, and content creation.
By understanding these prompting techniques, businesses can make more informed decisions about how to leverage AI models efficiently.
At Colne Data & AI, we specialise in helping businesses integrate and optimise the use of large language models such as OpenAI, Perplexity, Gemini, and Anthropic APIs. With so many variables involved—prompt design, task complexity, and balancing cost versus output quality—our expertise ensures that you get the most out of these powerful tools. We understand the nuances of zero-shot, one-shot, and few-shot prompting and how to apply them to drive efficiency, accuracy, and consistency in your business operations.
Whether you're automating customer support, generating reports, or producing marketing content, our team at Colne Data & AI can help you streamline these processes. We provide end-to-end integration of LLM APIs with your existing systems, ensuring seamless performance and cost optimisation. Our expertise in prompt engineering means we know how to craft the precise inputs needed to achieve your desired results—maximising both the quality and cost-effectiveness of your AI-powered solutions.
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