Prompt Engineering

Prompt engineering is a technique to improve the performance of language models by optimizing the input structure and formulation.

Prompt engineering is used in scenarios where enhancing the accuracy and relevance of responses from language models is crucial, such as in chatbots, virtual assistants, and automated content generation. It helps in guiding the model to produce more accurate and contextually appropriate outputs.

Prompt engineering is distinct from traditional machine learning techniques in that it focuses on optimizing the input to a pre-trained model rather than modifying the model itself. This allows users to leverage powerful general-purpose models for specific tasks without the need for extensive retraining.

Prompt engineering works by carefully crafting the input prompts given to the language model, including the choice of words, context, and format. This process involves iterative testing and refinement to identify the most effective prompts that yield the desired responses from the model. User input can be freely formulated or based on templates and best practices to increase the effectiveness of the query. Additionally, language models consider the previous conversation context, allowing for a continuous and coherent dialogue without losing context. Setting technical parameters like temperature helps controllling the creativity of the model’s response.

For example, a well-engineered prompt might assign a specific persona to the language model, specify concrete subtasks to be performed, use one or more examples to illustrate how the model should react to certain inputs, describe the target group, and specify the expected quality criteria like choice of words and others.

While generally accessible information is represented in the training data and thus contained in the model, specific tasks require detailed instructions and possibly additional information that may not be sufficiently represented in the training data. Hence, prompt engineering is an essential technique in natural language processing to build systems that can generate high-quality, relevant, and contextually appropriate text by optimizing the input prompts given to language models.