Dialogprompting

Dialogprompting is the practice of designing prompts to guide the behavior of conversational agents during interactive exchanges. It treats prompts as primary controls that influence the content, tone, scope, and decision-making of a model within a dialogue. In this framework, prompts can come from different sources, including system prompts that set overall goals or constraints, user prompts that specify user intent, and example prompts that illustrate desired responses.

Core concepts include system prompts, role or persona prompts, constraints, and the context window that carries prior turns. These elements work together to establish the agent's behavior, such as formality, accuracy requirements, or safety boundaries.

The typical workflow involves defining the task and safety requirements, assembling prompt templates, selecting few-shot exemplars, testing across diverse scenarios, and iterating based on results. Techniques commonly used are few-shot prompting with example dialogues, instruction-based prompts that specify allowed actions, and role-based prompts that fix the agent's voice. Constraint prompts can enforce style, content limits, or safety policies.

Applications span customer support chatbots, tutoring assistants, virtual helpers, interactive fiction, and data augmentation for training dialogue models. Benefits include greater control over responses, consistency across interactions, and faster iteration without retraining large models.

Challenges include prompt brittleness and sensitivity to wording, tendency to hallucinate or exhibit bias, safety and privacy concerns, context length limits, and reproducibility across systems. Evaluation relies on human judgments and automated metrics for relevance, correctness, and safety, complemented by A/B testing and prompt ablation studies.

Dialogprompting is a component of prompt engineering and is closely related to dialogue systems and conversational AI.