After 7 days letting AI agents control my email inbox... 📮


Summary

The speaker introduces an AI assistant for email management that learns user behavior, mimics responses through a digital user version, and improves over time. Initially built without code, the assistant leverages Zapier and GPT to automate email drafting. Challenges like low response quality are tackled through iterations, teaching the AI domain-specific knowledge, and observing GPT usage for business ideas. The creation of a knowledge base from past emails facilitates effective email response, with functions for extracting FAQs and a knowledge retrieval system. Further advancements include a decision-making AI agent for email categorization and specific actions based on predefined guidelines.


Introduction to AI Agent for Email Management

The speaker introduces an AI agent that manages email inboxes, reads and responds to emails, and improves its performance over time. The agent reads past emails to learn the user's behavior and creates a digital version of the user to mimic responses.

Building the First Version of the AI Assistant

The process of building the first version of the AI assistant without writing any code. The speaker uses Zapier to trigger GPT to generate email drafts automatically, reducing the manual effort of composing responses.

Challenges and Improvements

The speaker discusses the challenges faced with the initial AI assistant, including low response quality and incorrect responses. Iterations and teaching the AI about the user and domain-specific knowledge are highlighted for improvement.

Utilizing Existing Tools for Inspiration

The speaker mentions the utility of observing how people use GPT in various tasks to find business ideas. Reference to a research report by Hspot on chat GPT usage for business inspiration is provided.

Building the Second Version of the AI Assistant

The process of creating a knowledge base from past emails to train the AI assistant to mimic the user's behavior and respond to emails more effectively. The steps include extracting data, converting email files, and using language models for text parsing.

Enhancing the Knowledge Base with FAQs

Adding an extract FAQ function to extract facts and knowledge about the user from past emails. The steps involve creating functions to load data, extract FAQs, and save the information into CSV files.

Implementing a Knowledge Retrieval System

Creating a knowledge retrieval system using vector search in the knowledge base to enable the AI assistant to respond effectively to new emails. The use of Romus AI for managing and updating the knowledge base is explained.

Developing Decision-Making Abilities

Exploration of building an AI agent with decision-making abilities to categorize emails, take specific actions, and escalate when necessary. The process includes defining categories, actions, and creating tools for email handling.

Controlling the Agent's Behavior

Refinement of the AI agent to follow standard procedures by using custom tools to categorize emails accurately and take specific actions based on predefined guidelines. The process involves defining tools, categorizing emails, and creating a memory-initialized agent.

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