The text discusses how to build GenAI apps faster using ChatGPT and event streaming. The fundamental obstacle is that ChatGPT can't provide personalized answers to customer queries without access to the company's internal data stores. To overcome this, companies need to safely provide timely data from their internal data stores to ChatGPT. Event streaming is a good solution to bring all of these systems together, allowing companies to construct a unified view of each customer that's easy to query with low latency. The architecture involves using event streams to process raw information and then sink it into a relational database or document store. A retrieval-augmented generation (RAG) approach is used to connect policy data to ChatGPT by making an embedding out of the prompt itself and querying a vector database for related information. Plugins are also used to extend ChatGPT's capabilities, such as the Retrieval Plugin which acts as a proxy layer between ChatGPT and the vector database. The approach has limitations, including reliance on large context windows and vulnerability to prompt injection attacks. However, it enables companies to build real-time support agents that can take real action on their behalf.