Technical Sharing – AI with RAG

At Manifera, we organize monthly technical sharing sessions where team members dive into deep technical topics related to AI, aligning with relevant themes each month. These sessions provide a platform for learning, knowledge exchange, and collaboration among our talented team. This month, we’re excited to have Phi Tran, our Technical Architect, lead the session on RAG (Retrieval-Augmented Generation) to support AI applications. Phi presented how RAG enhances AI performance by combining data retrieval with generative models, offering valuable insights and practical approaches that can be applied in real-world AI projects.

What is RAG (Retrieval-Augmented Generation)?

RAG (Retrieval-Augmented Generation) is a method in artificial intelligence (AI) that combines two main steps:

1. Retrieval: The AI searches for and retrieves relevant information from a database or available knowledge source.
2. Generation: Using the retrieved data, the AI generates accurate and contextually relevant answers or content, often through language models like GPT.

The strength of RAG lies in its ability to provide more accurate and up-to-date information, making it especially useful for applications like chatbots, intelligent search engines, or content synthesis.

Highlights of the session included:

• What is RAG?: An overview of this innovative technique.
• How It Works: Combining data retrieval with generative models for smarter outputs.
• Key Applications: From intelligent search to conversational AI and beyond.
• Advantages & Challenges: Understanding its potential and how to address common obstacles.

The session ended with a lively Q&A, sparking great ideas for practical implementation.

Thank you, Phi Tran, for sharing your expertise and inspiring the team! 🌟