Say Goodbye to boring Chatbots by combining Structure (Bot Frameworks) & Flexibility (LLMs)

🌈 Abstract

The article discusses how to control the flow of a conversation with large language models (LLMs) like ChatGPT by combining a traditional chatbot framework with an AI agent.

🙋 Q&A

[01] Controlling the Conversation Flow with LLMs

1. How can we get an LLM to lead a user through a somewhat predefined conversation flow?

• The author defines the conversation flow structure using a chatbot framework like AWS Lex, which provides a simple drag-and-drop interface to map out the entire conversation.
• The author then integrates an AI agent, powered by an LLM like GPT-4, to mediate between the user and the rigid bot. The agent acts as an orchestrator, handling three key tasks:
  • Deciding whether to respond directly to the user or forward the message to the bot
  • Ensuring the first message is always directed to the bot
  • Addressing clarifying questions from the user directly without triggering the bot

2. How does the author implement memory to ensure smooth functioning of the system?

• The author serializes the agent's state (the history of messages) after each run and stores the serialized data in a database (Redis cache) with a session_id.
• For subsequent interactions, the agent reads from this stored data, allowing it to recall previous conversations.

3. What are some improvements the author suggests for the system?

• Memory optimization: Keeping just the system prompt along with the last 10 messages for every call to the LLM to reduce token usage and maintain the system prompt's accuracy.
• Guardrails: Implementing safeguards to prevent users from misusing the bot as a general-purpose Q&A system and maintain the bot's intended purpose.