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• RAG (Retrieval Augmented Generation)
• Embeddings + Vector
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1. Prompt Engineering

📌 What is it?

Writing smart input (prompt) to get correct output from LLM

🎯 Example

❌ Bad Prompt: Tell me about milk

✅ Good Prompt: You are a shop assistant.

Extract product name and quantity: Input: "2 milk and 1 bread"

Output JSON:

👉 Output becomes structured:

{ "milk": 2, "bread": 1 }

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2. RAG (Retrieval Augmented Generation)

Overview

Retrieval-Augmented Generation (RAG) is an AI architecture that enhances large language models (LLMs) by allowing them to retrieve relevant external information before generating a response.

Instead of relying only on pre-trained knowledge, RAG enables models to access up-to-date, domain-specific, and private data sources, making responses more accurate and context-aware.

RAG is widely used in:

• Customer support chatbots
• Healthcare report summarization
• Legal and compliance systems
• Financial analysis tools
• Enterprise knowledge search systems

Why RAG is Important

Traditional LLMs like GPT-style models generate responses based only on training data. This creates limitations:

Limitations of standard LLMs

• Knowledge cutoff (no real-time updates)
• Hallucinations (false or made-up answers)
• No access to private company data
• Lack of personalization/context

RAG solves these problems by:

• Fetching real-time relevant data
• Grounding answers in actual documents
• Reducing hallucinations
• Keeping knowledge updated without retraining

Real-Life Example

Imagine two students preparing for an exam:

Student 1 (LLM only)

• Reads books once
• Answers from memory only
• Cannot verify facts

Student 2 (RAG system)

• Reads books
• Can open books during exam
• Verifies answers in real-time

Student 2 performs better because they can retrieve information when needed.