The post provides a detailed tutorial on building and deploying a Slackbot capable of listening to conversations, learning from them, and answering questions based on the gathered knowledge within a Slack workspace. The process begins with setting up a basic Slack app using Python, Flask, and Slack Bolt SDK, and involves the use of ngrok for local development. It guides through configuring permissions, setting up environment variables, and extends to using LlamaIndex for storing and querying facts. The Slackbot evolves to reply only when mentioned, store facts persistently using Qdrant, and prioritize recent messages to handle evolving conversations. The tutorial concludes with deploying the Slackbot on Render for production use, offering ideas for further enhancements such as multi-channel operations, contextual tagging, and multi-modal capabilities, emphasizing the flexibility and potential of Slackbots integrated with advanced data indexing and query tools.