2025-02-15 | By Mariusz Jażdżyk
AI-powered services, despite their immense popularity, are built from relatively simple components. These systems generate responses by predicting the next words in a sequence, forming coherent and contextually relevant answers.
While applications leveraging these AI components are impressive, they primarily consist of stateless functions. In their basic form, these functions do not retain memory of previous interactions, making it challenging to maintain continuity in conversations.
The success of AI-driven applications depends on various factors, with one of the most crucial being the ability to retain conversational context. Initially, this is implemented for individual users by tracking their previous messages and conversation flow to ensure coherence. However, as the system scales, a new challenge arises—how does it handle 10, 100, or even thousands of users simultaneously?
Implementing effective conversational AI systems requires robust data modeling and efficient conversation history management. To address this challenge, we opted for a dual NoSQL database approach—one optimized for cloud environments and the other for on-premise infrastructure.
This setup allows the system to manage thousands of simultaneous conversations while preserving the unique context of each interaction.
This approach mirrors solutions employed by leading AI companies such as OpenAI, Perplexity, and Google. Although a seemingly small function, context management significantly enhances AI interactions—and this is just the beginning of an evolving solution.
Author: Mariusz Jażdżyk
The author is a lecturer at Kozminski University, specializing in building data-driven organizations in startups. He teaches courses based on his book Chief Data Officer, where he explores the practical aspects of implementing data strategies and AI solutions.
