1. What is RAG?

RAG (Retrieval-Augmented Generation) is a technique that combines an LLM with an external knowledge source. Instead of depending only on information learned during model training, the LLM retrieves relevant information and uses it to generate an answer.

User Query
    ↓
Retriever
    ↓
Relevant Documents
    ↓
LLM
    ↓
Generated Answer

2. Why RAG is Needed

LLMs can have limitations such as:

Outdated knowledge
Hallucinations
Lack of private or domain-specific information
Limited access to external databases
Knowledge cutoff limitations

RAG addresses these problems by retrieving information from an external knowledge source.

Without RAG

Question
   ↓
LLM
   ↓
Answer

With RAG

Question
   ↓
Retrieve Information
   ↓
Relevant Context
   ↓
LLM
   ↓
Answer

3. RAG + LLM Architecture

A typical RAG system contains:

              RAG System
                  ↓
        ┌─────────┴─────────┐
        ↓                   ↓
   Knowledge Base        LLM
        ↓                   ↑
    Embeddings              │
        ↓                   │
   Vector Database          │
        ↑                   │
        └── Retriever ──────┘
                 ↑
              Query

4. RAG Pipeline

Step 1: Collect Documents

Documents can come from:

PDFs
Websites
Databases
Text files
Company documents
Documentation
Knowledge bases

Documents
    ↓
Knowledge Base

Step 2: Document Chunking

Large documents are divided into smaller pieces called chunks.

Large Document
      ↓
 ┌────┼────┐
 ↓    ↓    ↓
Chunk 1
Chunk 2
Chunk 3

Chunking makes retrieval more efficient.

Step 3: Create Embeddings

Each chunk is converted into a numerical vector.

Text
 ↓
Embedding Model
 ↓
Vector

For example:

"RAG combines retrieval and generation"
             ↓
[0.21, -0.14, 0.72, ...]

Similar meanings should produce vectors that are close together.

5. Vector Database

The embeddings are stored in a vector database. Examples include:

FAISS
Chroma
Pinecone
Weaviate
Milvus

Document
   ↓
Chunk
   ↓
Embedding
   ↓
Vector Database

The vector database allows relevant information to be retrieved efficiently.

6. Retrieval

When a user asks a question, the question is also converted into an embedding.

User Query
    ↓
Query Embedding
    ↓
Vector Database
    ↓
Similarity Search
    ↓
Top-K Relevant Chunks

For example:

Query:
"What is RAG?"

Retrieved:

1. RAG combines retrieval with generation
2. RAG uses external knowledge
3. RAG can reduce hallucinations

7. Augmentation

The retrieved documents are added to the user’s question as context.

Question
   +
Retrieved Documents
   ↓
Context

Example:

Question:
"What is RAG?"

Context:
"RAG combines information retrieval
with language generation."

        ↓

Prompt
        ↓

LLM

8. Generation

The LLM receives the question and retrieved context.

Query
  +
Context
  ↓
LLM
  ↓
Answer

Example:

Question:
"What is RAG?"

Context:
"RAG retrieves external information
before generating a response."

Answer:
"RAG is a technique that retrieves
external information and provides it
to an LLM to generate an answer."

9. RAG vs Traditional LLM

Traditional LLM	RAG + LLM
Uses learned model knowledge	Uses model + external knowledge
Knowledge can become outdated	Can retrieve updated information
Difficult to access private data	Can connect to private knowledge bases
Higher risk of unsupported answers	Retrieved context can ground answers
No retrieval step	Includes retrieval
Simpler architecture	More complex architecture

10. RAG vs Fine-Tuning

RAG and fine-tuning solve different problems.

RAG

Used when external knowledge needs to be retrieved.

Knowledge
   ↓
Database
   ↓
Retriever
   ↓
LLM

Fine-Tuning

Used to modify model behavior or specialize its responses.

Training Dataset
      ↓
Fine-Tuning
      ↓
Specialized Model

A system can also use both:

Fine-Tuned LLM
      +
    RAG
      ↓
Specialized RAG System

11. Types of RAG

Basic RAG

Query
 ↓
Retriever
 ↓
Documents
 ↓
LLM

Hybrid RAG

Combines different retrieval methods.

        Query
          ↓
   ┌──────┴──────┐
   ↓             ↓
Keyword       Vector
Search        Search
   ↓             ↓
   └──────┬──────┘
          ↓
      Retrieved
      Documents

Graph RAG

Uses a knowledge graph to represent relationships between entities.

Documents
    ↓
Knowledge Graph
    ↓
Related Entities
    ↓
LLM

Multimodal RAG

Retrieves information from multiple data types.

Text
Images
Tables
PDFs
  ↓
Multimodal Retrieval
  ↓
Multimodal LLM

12. RAG and Hallucination

A hallucination occurs when an LLM generates information that is unsupported or incorrect. RAG can help ground an answer using retrieved information.

Question
   ↓
Relevant Context
   ↓
LLM
   ↓
Grounded Answer

However, RAG does not automatically eliminate hallucinations. Problems can still occur if:

Bad Retrieval
     ↓
Wrong Context
     ↓
Wrong Answer

Therefore, both retrieval and generation need to be evaluated.

13. RAG Evaluation

Important evaluation areas include:

Retrieval

Recall@K
Precision@K
MRR
NDCG

Generation

Correctness
Relevance
Faithfulness

System

Latency
Cost
Token Usage
Safety

Overall:

             RAG Evaluation
                   ↓
       ┌───────────┼───────────┐
       ↓           ↓           ↓
   Retrieval   Generation    System
       ↓           ↓           ↓
    Recall     Faithfulness  Latency
   Precision    Relevance      Cost
      MRR       Correctness   Safety

14. RAG Applications

RAG is commonly used for:

Document question answering
Customer support
Enterprise search
Research assistants
Code documentation
Legal document search
Medical knowledge systems
Educational assistants
Internal company knowledge bases

Example:

Company Documents
       ↓
Vector Database
       ↓
RAG
       ↓
Company Assistant

15. RAG With Your Previous FAISS Learning

A simple RAG system can use the same components studied earlier:

Documents
    ↓
Sentence Transformer
    ↓
Embeddings
    ↓
FAISS
    ↓
Similarity Search
    ↓
Relevant Documents
    ↓
LLM
    ↓
Answer

For example:

all-MiniLM-L6-v2
        ↓
   Embeddings
        ↓
      FAISS
        ↓
Top relevant chunks
        ↓
Hugging Face LLM
        ↓
Generated response

16. Complete RAG Flow

                Documents
                    ↓
                 Chunking
                    ↓
                Embeddings
                    ↓
              Vector Database
                    ↓
User Query → Query Embedding
                    ↓
                 Retrieval
                    ↓
             Relevant Context
                    ↓
             Prompt + Context
                    ↓
                   LLM
                    ↓
               Final Answer

17. Important RAG Components

Component	Purpose
Documents	Source of knowledge
Chunking	Split documents
Embeddings	Convert text into vectors
Vector Database	Store and search vectors
Retriever	Find relevant information
Reranker	Improve retrieved results
Prompt	Combine query and context
LLM	Generate final answer
Evaluator	Measure system quality

18. Key Takeaway

The relationship can be remembered as:

RAG = Retrieval + LLM Generation

The LLM provides the language understanding and generation, while RAG provides access to external knowledge.

External Knowledge
       +
   Retrieval
       +
      LLM
       ↓
  RAG System

​1. What is RAG?

​2. Why RAG is Needed

​3. RAG + LLM Architecture

​4. RAG Pipeline

​Step 1: Collect Documents

​Step 2: Document Chunking

​Step 3: Create Embeddings

​5. Vector Database

​6. Retrieval

​7. Augmentation

​8. Generation

​9. RAG vs Traditional LLM

​10. RAG vs Fine-Tuning

​RAG

​Fine-Tuning

​11. Types of RAG

​Basic RAG

​Hybrid RAG

​Graph RAG

​Multimodal RAG

​12. RAG and Hallucination

​13. RAG Evaluation

​Retrieval

​Generation

​System

​14. RAG Applications

​15. RAG With Your Previous FAISS Learning

​16. Complete RAG Flow

​17. Important RAG Components

​18. Key Takeaway

1. What is RAG?

2. Why RAG is Needed

3. RAG + LLM Architecture

4. RAG Pipeline

Step 1: Collect Documents

Step 2: Document Chunking

Step 3: Create Embeddings

5. Vector Database

6. Retrieval

7. Augmentation

8. Generation

9. RAG vs Traditional LLM

10. RAG vs Fine-Tuning

RAG

Fine-Tuning

11. Types of RAG

Basic RAG

Hybrid RAG

Graph RAG

Multimodal RAG

12. RAG and Hallucination

13. RAG Evaluation

Retrieval

Generation

System

14. RAG Applications

15. RAG With Your Previous FAISS Learning

16. Complete RAG Flow

17. Important RAG Components

18. Key Takeaway