🚀 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.
📚 Example
Question:
Plain Text
1
What is our refund policy?
Show more lines
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
- 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
🧰 Tools
Agentic AI can:
- Search
APIs
- Access
databases
- Perform
RAG retrieval
- Execute
workflows
Example
User Request:
Plain Text
1
Book me the cheapest flight
Show more lines
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.
No comments:
Post a Comment