Scripts - Chat

Chat scripts have long served as the foundational architecture for rule-based conversational agents, from early customer support bots to modern interactive fiction. However, the proliferation of Large Language Models (LLMs) has challenged the traditional deterministic script model. This paper examines the evolution of chat scripts, contrasting static, linear scripts with dynamic, adaptive scripting methods. We analyze the trade-offs between control (predictability, safety) and flexibility (user satisfaction, error recovery). Our findings suggest that a hybrid model—where a script defines high-level "states" and an LLM fills the low-level utterances—offers the optimal balance for production systems. We conclude with design recommendations for next-generation chat script architectures.

With LLMs (GPT-4, Llama 3, etc.), a new paradigm has emerged: . Instead of hardcoded branches, the script is a natural language instruction that the LLM follows dynamically. chat scripts

Historically, these were simple "if-this-then-that" trees used by early customer service software. Today, they have evolved into sophisticated frameworks used by both humans and generative AI agents to maintain brand tone, solve problems quickly, and provide a seamless user experience. 2. Types of Chat Scripts and Use Cases Chat scripts have long served as the foundational

However, generative scripts introduce new problems: With LLMs (GPT-4, Llama 3, etc

Workflow example:

Avoid jargon, complex sentence structures, or ambiguity. The goal is to transfer information with the lowest possible cognitive load on the user.