There are several techniques that can be used to generate text that adheres to specific guidelines or constraints. Here are a few examples:

  1. Rule-based generation: This approach involves creating a set of rules or constraints that dictate how the text should be generated. These rules can be based on grammar, syntax, vocabulary, or any other specific guidelines. For example, if you want to generate text that follows a specific writing style, you can define rules for sentence structure, word choice, and tone.

  2. Template-based generation: In this approach, you define a set of templates or placeholders that represent different parts of the text. The generator then fills in these templates with appropriate words or phrases based on the given guidelines. For instance, if you want to generate product descriptions, you can have templates for features, benefits, and pricing, and the generator can populate them based on the specific product and target audience.

  3. Machine learning-based generation: This technique involves training a machine learning model on a large corpus of text that adheres to the desired guidelines. The model learns the patterns and structures present in the training data and can generate text that follows similar guidelines. For example, if you want to generate news articles in a specific style, you can train the model on a dataset of news articles written in that style.

  4. Constraint-based generation: This approach involves defining constraints or requirements for the generated text. These constraints can be related to length, word choice, topic, or any other desired characteristic. The generation process then optimizes for generating text that meets these constraints. For instance, if you want to generate product reviews of a certain length, you can set the constraint of a specific word count.

  5. Language generation models: Advanced language generation models, such as OpenAI's GPT, can generate text by predicting the next word or phrase based on the context provided. These models are trained on large amounts of text and can generate coherent and contextually relevant responses. By fine-tuning these models on specific guidelines, they can be used to generate text that adheres to those guidelines.

  It is important to note that the effectiveness of these techniques depends on the quality and quantity of the training data, the complexity of the guidelines, and the specific requirements of the text generation task. Experimentation and fine-tuning may be required to achieve the desired results.