Five Common Prompting Techniques for Large Language Models (LLMs)

LLM Prompting

  1. Zero-shot prompting: This technique involves giving the model a task with no examples or prior information. The model relies entirely on its pre-existing knowledge to generate a response.

    • Example: “Translate the following sentence to French: 'I am learning how to code.'”
  2. One-shot prompting: In this approach, the model is provided with one example of the task before being asked to generate a response. This helps the model understand the desired format and context.

    • Example: “Translate the following sentence to French. Example: 'I love programming.' → 'J'aime programmer.' Now, translate: 'I am learning how to code.'”
  3. Few-shot prompting: This technique involves giving the model several examples of the task to help it understand the context and format better. It is particularly useful for complex tasks.

    • Example: “Translate the following sentences to French. Example 1: 'I love programming.' → 'J'aime programmer.' Example 2: 'She enjoys reading books.' → 'Elle aime lire des livres.' Now, translate: 'I am learning how to code.'”
  4. Instruction-based prompting: This method uses clear and explicit instructions to guide the model's output. It focuses on detailing the task requirements and expectations.

    • Example: “Please translate the following sentence into French: 'I am learning how to code.' Ensure the translation is accurate and maintains the original meaning.”
  5. Chain-of-thought prompting: This technique encourages the model to think through the problem step-by-step, which can be helpful for complex or multi-step tasks. It guides the model to break down the process into logical steps.

    • Example: “Translate the sentence 'I am learning how to code' into French. First, identify the subject ('I'), then the verb phrase ('am learning'), and finally the object ('how to code'). Now, translate each part and combine them into a coherent sentence.”
  6. Big-Ass-Prompt: Often referred to as “BAP” or “Big Prompt,” is a technique used to improve the performance of large language models by providing extensive and detailed context or examples within the prompt. This method leverages the model's ability to understand and generate more accurate responses by giving it a substantial amount of information to work with.

    • Example: Suppose the task is to generate a summary of a given article. A Big Ass Prompt might look like this:

“Please summarize the following article. First, read the entire article carefully. Identify the main points, key arguments, and important details. Then, write a concise summary that includes the following:

1. The main topic of the article. 2. The primary arguments or points made by the author. 3. Any significant data or statistics mentioned. 4. The conclusion or final thoughts of the author.

Here is the article: [Insert full article text]

Example Summaries:

'The article discusses the impact of climate change on global agriculture. It highlights the increasing frequency of extreme weather events and their effects on crop yields. Key data from the article includes a 20% reduction in wheat production in certain regions. The author concludes by emphasizing the need for sustainable farming practices to mitigate these impacts.' 'This article explores the rise of remote work in the tech industry. It outlines the benefits such as increased flexibility and cost savings for companies, as well as challenges like maintaining team cohesion. The article cites a survey where 60% of respondents preferred remote work over traditional office setups. The conclusion stresses the importance of adapting management strategies to support remote teams effectively.'

Now, summarize the provided article in a similar format.”

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