✍️
🚀 Introduction
One of the biggest challenges with AI is accuracy.
This problem is addressed by:
👉 RAG (Retrieval-Augmented Generation)
🎯 Objective: GROUND
Instead of relying only on the model's memory, RAG retrieves information from external sources first.
🔄 Workflow
📚 Example
Question:
GenAI may guess.
RAG will:
✅ Benefits
More Accurate
Uses real company documents.
Up-to-Date
Can access latest information.
Less Hallucination
Grounded answers reduce incorrect outputs.
Citations
Can provide document references.
🎯 Conclusion
When correctness matters, RAG is often a better choice than using an LLM alone.
Agentic AI – Moving Beyond Answers to Actions
🚀 Introduction
GenAI creates.
RAG retrieves.
Agentic AI executes.
This is the next major step in AI evolution.
🎯 Objective: ACT
Agentic AI focuses on completing tasks instead of simply answering questions.
🏗 Architecture
The image shows key Agentic AI components:
Memory
Stores:
- Short-term context
- Long-term knowledge
Planning
Uses:
- ReAct
- Chain of Thought
- Multi-step reasoning
Agent
Acts as the decision-maker.
🧰 Tools
Agentic AI can:
- Search APIs
- Access databases
- Perform RAG retrieval
- Execute workflows
🔄 Agent Flow
Example
User Request:
Agent Actions:
- Search flights
- Compare prices
- Choose option
- Book ticket
- Confirm booking
🎯 Conclusion
Agentic AI is designed for end-to-end execution, not just information generation.
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