Justina Petraitytė
Data Scientist, developer, and educator with a passion for enabling developers to build great applications and turn data into meaningful insights and innovative products. With over the 10 years spent in Data Science and Developer Relations for AI and Web3 spaces, Justina has been focusing on empowering developers around he world to build better applications and products.
Sessions
LLMs offer powerful capabilities, but deploying them effectively in production remains a challenge for conversational AI and Chatbot applications, especially when it comes to minimizing hallucinations and ensuring accurate responses. In this 90-minute hands-on tutorial, we’ll explore building conversational AI systems using CALM and Rasa. CALM (Conversational AI Language Model) combines traditional conversational AI techniques with LLMs, separating conversational ability from business logic execution to deliver reliable, cost efficient, and scalable solutions. Unlike LLMs that handle both sides of the conversation, CALM focuses on user understanding with predefined business logic. This approach not only accelerates development but also enhances cost efficiency, scalability and reliability. By focusing on predefined business logic with CALM, you’ll gain the ability to build sophisticated, scalable systems faster. You’ll also learn how to use fine-tuned, open-weight models, such as llama 8b to power your AI assistant.
Participants will learn how to use CALM for business logic and Rasa for dialogue management, with practical insights, code examples, and best practices. Materials will be provided via a GitHub repository with a GitHub Codespace for easy access and execution.