Prompt templates are structured, reusable text formats that serve as a blueprint or "recipe" for generating prompts. They contain placeholders (variables) that are filled in with specific data or context before being sent to a language model. This approach ensures consistency, guides the model's response structure, and simplifies the process of creating effective prompts for various tasks. By defining the context, instructions, and input data format, templates guide the model to produce more predictable and relevant outputs.

A. Prompt templates are text-based structures with placeholders, not complex algorithms that require compilation like source code.

C. The fundamental purpose of a template is to be modified and customized by filling in variables with specific context or data.

D. Prompt templates are designed primarily for textual content, as this is the native input format for language models.

1. Official Vendor Documentation: Oracle Cloud Infrastructure (OCI) Documentation, "Generative AI - Prompt Engineering". The documentation explains that a prompt consists of instructions and context, which can be structured for reuse. This structured format with placeholders is functionally a template or recipe. The guide states, "A good prompt gives the model enough context... You can also provide few-shot examples in the prompt to guide the model." This practice of creating a structured, reusable format is the essence of a template. (See OCI Documentation > AI and Machine Learning > Generative AI > Key Concepts > Prompt Engineering).

2. Academic Publication: Liu, P., Yuan, W., Fu, J., et al. (2023). "Pre-train, Prompt, and Predict: A Systematic Survey of Prompting Methods in Natural Language Processing." ACM Computing Surveys, 55(9), Article 195, p. 6. The paper discusses "Template Construction," describing templates as textual strings containing slots (placeholders) for input text. It states, "The first step of prompt-based learning is to design a template, which is a textual string that has two slots...". This definition aligns directly with the concept of a predefined recipe. DOI: https://doi.org/10.1145/3560815

3. University Courseware: Stanford University, CS324 - Large Language Models, Winter 2022, "Lecture 3: Prompting, Instruction Finetuning". The course materials discuss how prompts are structured to elicit desired behavior. The concept of creating a format (e.g., "Translate English to French: {englishtext} =>") is presented, which is a form of templating or a recipe for a specific task.