Ramsud Technologies: Tech Meetup Group

Curious how AI tools give accurate, up-to-date answers instead of guessing?
That’s where Retrieval-Augmented Generation (RAG) comes in.

In this beginner-friendly session, you’ll learn how modern AI systems combine search + generation to produce reliable results—and how you can build one yourself.

We’ll break down complex concepts into simple ideas and then walk through how RAG works in real applications like chatbots, document search, and knowledge assistants.
By the end of the session, you’ll clearly understand how to connect large language models with external data and improve response accuracy.

### What You’ll Learn

• What RAG is and why it matters
• Limitations of traditional AI (hallucinations, outdated knowledge)
• How retrieval + generation works together
• Key components: embeddings, vector databases, LLMs
• Real-world use cases (chatbots, search systems, internal tools)
• High-level architecture of a RAG system

***

### Who Should Attend

• Developers curious about AI/LLMs
• Students and beginners exploring AI
• Backend engineers building intelligent systems
• Anyone interested in building smarter, more reliable AI applications

***

### Prerequisites

• Basic understanding of programming (Python preferred)
• No prior AI/ML knowledge required

***

### Agenda

• Introduction to AI & current limitations
• Understanding RAG (simple explanation)
• How RAG works step by step
• Demo / architecture walkthrough
• Q&A

Curious how AI tools give accurate, up-to-date answers instead of guessing?
That’s where Retrieval-Augmented Generation (RAG) comes in.

In this beginner-friendly session, you’ll learn how modern AI systems combine search + generation to produce reliable results—and how you can build one yourself.

We’ll break down complex concepts into simple ideas and then walk through how RAG works in real applications like chatbots, document search, and knowledge assistants.
By the end of the session, you’ll clearly understand how to connect large language models with external data and improve response accuracy.

### What You’ll Learn

• What RAG is and why it matters
• Limitations of traditional AI (hallucinations, outdated knowledge)
• How retrieval + generation works together
• Key components: embeddings, vector databases, LLMs
• Real-world use cases (chatbots, search systems, internal tools)
• High-level architecture of a RAG system

***

### Who Should Attend

• Developers curious about AI/LLMs
• Students and beginners exploring AI
• Backend engineers building intelligent systems
• Anyone interested in building smarter, more reliable AI applications

***

### Prerequisites

• Basic understanding of programming (Python preferred)
• No prior AI/ML knowledge required

***

### Agenda

• Introduction to AI & current limitations
• Understanding RAG (simple explanation)
• How RAG works step by step
• Demo / architecture walkthrough
• Q&A