Ultimate Guide to Effective Prompt Engineering — Serpstat Blog

What are Prompts? 

Prompts are artificial stimuli that elicit a response or behavior from another entity, the user. In AI-based language generation systems, prompts collect information about the user’s language and generate new language samples that have never been seen before. Prompt elements can include natural language, code, and multimodal prompts incorporating images, videos, and other media.

How to Use Prompts Effectively

As a result of the training methods and data used for instruction-following ******, specific prompt formats have proven to be highly effective and closely aligned with the intended tasks.

Compiled by OpenAI for API, these prompt formats produce reliable results:

• Instructions should be placed at the beginning of the prompt. Use ### or “”” to separate instruction and context sections.
  

Source: https://help.openai.com/en/articles/6654000-best-practices-for-prompt-engineering-with-openai-api



• Be specific, descriptive, and detailed about the desired context, outcome, length, format, and style.
  
• Articulate the desired output through examples, specifying the required format and structure.
  
• Start with zero-shot or few-shot examples before considering fine-tuning.
  
• Avoid imprecise descriptions. Use concise and clear instructions instead.
  
• Instead of focusing on what not to do, guide what to do instead.
  
• Use “leading words” to guide the model toward a specific pattern or language for code generation.
  
• Define stop sequences to signal the end of the text generation process.
  

Elements of a Well-Designed Prompt

As we delve deeper into prompt engineering, we discover that prompts typically contain certain elements. These elements include:

• instruction that specifies the task or action the model should perform;
  
• the context that provides additional information to help the model produce better responses;
  
• input data which comprises questions/information we want the model to process;
  
• output indicator, which specifies the type or format of the output required. 
  

Including all four elements in a prompt is unnecessary, and the format may vary depending on the task. In upcoming guides, we’ll explore more concrete examples of prompts.

Let’s also familiarize ourselves with some key terms.

• ******/Engine. Imagine ****** as skilled individuals with distinct abilities. For instance, “text-davinci” and “code-davinci.”
  
• Tokens. Bite-sized units of language that the API processes.
  
• Zero-shot prompting. A commonly used approach in ChatGPT t prompt engineering, where users ask questions directly without providing specific instructions. In this method, the model generates a coherent response based on the question, but users have limited control over the accuracy and format of the response.
  
• Chain-of-thought prompting. A style of few-shot prompting, where the prompt contains a series of intermediate reasoning steps. Chain-of-thought prompting encourages the model to reason similar to the way that the prompt is written, that is in a series of steps.
  

Practical Tips for Designing Prompts

Here are some helpful recommendations for designing effective prompts:

• Be specific: A well-written prompt should be specific and clear. It should leave no room for ambiguity or guesswork.
  
• Consider the context: The context of the prompt can play a significant role in shaping the output. Providing enough context to help the model produce relevant and accurate responses.
  
• Use natural language: AI ****** are designed to analyze and understand natural language. Therefore, using plain, straightforward language instead of complex jargon is best.
  
• Test your prompts: Before using the prompt on a large scale, it’s essential to test it on a smaller sample size. This way, you can evaluate its effectiveness and make changes if necessary.
  
• Keep it simple: A too complex or convoluted prompt can confuse the model and lead to inaccurate output. To avoid this, it’s best to keep the prompt straightforward.
  
• Be mindful of bias: AI ****** can be affected by discrimination in the data used to train them. Therefore, it’s essential to be conscious of any potential bias in the prompt and strive to create fair and unbiased prompts.
  

Crafting Effective Prompts Examples for Desired Responses

This section will provide different examples to help understand how prompts can be used to achieve various tasks and introduce significant concepts. Using AI is often the best way to learn an idea. The following examples illustrate how well-crafted prompts can be used to accomplish various types of tasks:

• Sentiment analysis
  
• Language translation
  
• Text generation
  
• Image captioning
  
• Text summarization
  
• Communication
  
• Named entity recognition
  
• Keyword extraction
  

Sentiment Analysis

Sentiment analysis identifies the sentiment expressed in a text, whether positive, negative, or neutral. This analysis is essential in understanding how customers perceive a product or service. 

The following is an example of a prompt that can be used to perform sentiment analysis:

Language Translation

Language translation is the task of converting text from one language to another. The following is an example of a prompt that can be used to translate a sentence from English to German:

Text Generation

Text generation is the task of generating new text based on some given input. The following is an example of a prompt that can be used to create a new text: