# Evolution of the Udemy AI Assistant Intent Understanding System

DevFeed: [Evolution of the Udemy AI Assistant Intent Understanding System](<https://devfeed.tech/articles/evolution-of-the-udemy-ai-assistant-intent-understanding-system-26352.md>)

Original publisher: [Read original article](<https://medium.com/udemy-engineering/evolution-of-the-udemy-ai-assistant-intent-understanding-system-ec3ee0039364?source=rss----19c6d3367ed4---4>)

Author: Jack Kwok

Published: 2025-05-28T16:35:59Z

Content type: article

Language: en

Sources: [Udemy Engineering](<https://devfeed.tech/sources/udemy-engineering.md>)

Topics: [Artificial Intelligence](<https://devfeed.tech/topics/ai.md>), [Language models](<https://devfeed.tech/topics/language-models.md>), [context](<https://devfeed.tech/topics/context.md>), [API](<https://devfeed.tech/topics/api.md>), [Development](<https://devfeed.tech/topics/development.md>)

Tags: [ai](<https://devfeed.tech/tags/ai.md>), [api](<https://devfeed.tech/tags/api.md>), [artificial-intelligence](<https://devfeed.tech/tags/artificial-intelligence.md>), [chat](<https://devfeed.tech/tags/chat.md>), [chatbots](<https://devfeed.tech/tags/chatbots.md>), [context](<https://devfeed.tech/tags/context.md>), [cost](<https://devfeed.tech/tags/cost.md>), [development](<https://devfeed.tech/tags/development.md>), [function](<https://devfeed.tech/tags/function.md>), [generation](<https://devfeed.tech/tags/generation.md>), [language](<https://devfeed.tech/tags/language.md>), [language-models](<https://devfeed.tech/tags/language-models.md>), [llm](<https://devfeed.tech/tags/llm.md>), [llms](<https://devfeed.tech/tags/llms.md>), [models](<https://devfeed.tech/tags/models.md>), [quality](<https://devfeed.tech/tags/quality.md>), [responses](<https://devfeed.tech/tags/responses.md>), [search](<https://devfeed.tech/tags/search.md>), [speed](<https://devfeed.tech/tags/speed.md>), [udemy](<https://devfeed.tech/tags/udemy.md>)

## AI overview

This article describes the evolution of Udemy's AI Assistant intent-understanding system. It explains how the system predicts a learner's intent and invokes chains of actions to retrieve relevant context from data sources and APIs, including lecture summaries and course search, while balancing response quality, speed, and cost.

## Source excerpt

Introduction Imagine having a virtual learning companion by your side every time you tackle a new course or challenging concept, ready to clarify doubts, summarize lessons, or quiz your understanding on demand. This isn't just an idea for the future. Thanks to the Udemy AI Assistant, it's a reality for every Udemy learner today. Powered by AI, the Assistant is actively supporting learners all over the world, guiding them step by step through their skills development journeys and making learning smoother, faster, and more personalized than ever. Behind the scenes, delivering such smart, relevant help relies on one crucial ability: understanding exactly what the learner wants. From course-related clarifications to lecture summaries and beyond, the AI Assistant must accurately grasp the intent behind every question to deliver the perfect response. In this article, we'll take you behind the curtain to explore how we've evolved the Udemy AI Assistant's intent understanding system. You'll discover key lessons from our journey: the challenges we faced as we expanded features, breakthroughs that boosted accuracy, and how we balanced response quality, speed, and cost. Background With Large Language Models (LLMs), setting the relevant context in the prompt is key to eliciting high-quality, high-relevance chat responses. To that end, we enable the AI Assistant to connect to various data sources and services to obtain the most relevant context. Behind the scenes, the AI Assistant predicts the learner's intent and then invokes the corresponding chain of actions to fetch the most helpful context from data sources and APIs. For example, if the learner intends to ask for a summary of the current video lecture, a chain of actions would include invoking an API call to fetch the pre-generated lecture summary and send it to the LLM for final response generation. As another example, if the learner intends to search for a topic within the course, a function call will execute a search t