Building AI Applications in the Enterprise Part 1 | WSO2Con Barcelona 2025
AI Summary
Summary of Video: Generative AI Applications
Introduction
- Overview of practical use cases of Generative AI (GenAI).
- Discussion of the evolution from simple to complex use cases and integration knowledge.
Key Concepts
- Generative AI (GenAI): A subset of AI that generates new content (text, images, audio) based on existing patterns.
- Large Language Models (LLMs): Specialize in text generation, e.g., the impact of the “Attention is All You Need” paper.
Practical Use Case: AI Assistants
- Examples of AI features integrated into applications (e.g., Q&A sessions, session advisor, expert finder).
- Implementation Steps:
- Design user interactions and prompts.
- Develop an HTTP service for chat functionality.
- Use OpenAI’s API for model integration and responses.
Challenges & Solutions
- Hallucination Problem: Risk of generating incorrect answers due to training data limitations.
- Suggested solutions:
- Fine-tuning models (complex and time-consuming).
- In-context learning: Incorporate real-time data via prompts.
Advanced Techniques
- Retrieval Augmented Generation (RAG): Enhances GenAI applications by filtering relevant data before generation.
- Use of vector databases for data retrieval and embedding for efficient information processing.
AI Agent Integration
- Concept of AI agents that dynamically choose tools to execute tasks based on user queries.
- Importance of integrating various data sources to enhance application capabilities (e.g., fetching speaker information and session details).
Conclusion
- Successful GenAI applications require accuracy, acceptable latency, scalability, secure data access, and the ability to avoid misuse.
- Integration approaches should balance complexity with risk management, ensuring tools are agent-compatible (e.g., restructuring APIs for better agent usability).