What are some of the different prompt engineering techniques?

Prompt engineering refers to designing and refining prompts for language models to guide their behavior for the desired output. It involves crafting well-structured inputs or instructions that elicit preferred responses from the model. Prompt engineering aims to improve the generated outputs’ control, specificity, and relevance by providing explicit cues, constraints, or context to the language model.

Two basic principle aspects can be followed to have a productive output:

• Specifying clear instructions as prompts.

• The model should be allowed to think (to take time) and produce results.

Prompt engineering techniques

Prompt engineering is growing and new techniques are being introduced rapidly. Based on the above-mentioned principle aspects, some state-of-the-art techniques are described in the subsequent section.

Basic prompting

The two basic or explicit instruction prompts guide the language model with explicit instructions that specify the desired output. These approaches for prompting include zero-shot and few-shot prompting. Let’s explore these two before diving into more advanced approaches to prompt a model.

• Zero-shot prompting: The language model generates output or makes predictions without explicit examples or training. This technique is effective for straightforward problems like classification, text generation, text transformation, etc., as shown below:

• Few-shot prompting: This approach employs a set of examples or small training data to guide the language model for desired output. This technique is effective where we can’t describe the expected output from the language model but still want an answer. Therefore, while prompting, we let the model process some examples and steer it to generate a response. The example is given below:

Chain-of-thought prompting

Chain-of-thought prompting involves providing a sequence of prompts to generate a coherent and continuous response. Firstly, the model is provided with an initial prompt to initiate the generation process. Then, additional prompts are sequentially introduced, each subsequent prompt influenced by the model’s previous response. This technique aims to replicate the flow of a genuine conversation by using prompts as follow-up questions or comments that build upon the model’s prior statements. An example of such prompting is given below:

Generated knowledge prompting

Generated knowledge prompts enable the models to integrate or incorporate previous knowledge to improve the results. To do so, a user needs to generate the knowledge by prompting helpful information about the topic and then leverage this knowledge to generate a final response.

For example, if we want a language model to write an article on cybersecurity, we will first prompt the model to generate some facts, types, or techniques for cybersecurity. As a result, the model will generate a more effective response by integrating the provided knowledge before the final prompt, as illustrated below:

Comparative analysis

Conducting a comparative analysis of different prompt engineering approaches entails evaluating and comparing various strategies and techniques employed in designing and refining prompts for language models. The following table analyzes all the aforementioned techniques:

It is important to emphasize that the choice of prompt engineering approach relies on the specific task requirements, desired outcomes, and the attributes of the language model employed. Conducting iterative experimentation and evaluation is vital in honing prompt design and attaining optimal results.