Summarize by Aili
Say Goodbye to boring Chatbots by combining Structure (Bot Frameworks) & Flexibility (LLMs)
๐ 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.
๐ 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.
Shared by Daniel Chen ยท
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