Introduction
Large Language Models (LLMs) such as GPT, Gemini, Claude, and other generative AI models can understand questions and generate remarkably useful answers. However, an LLM has an important limitation:
An LLM does not automatically know your organization's private, frequently changing, or newly created information.
For example, imagine a company has:
10,000 internal documents
HR policies
Product manuals
Customer records
Technical documentation
Financial reports
Support tickets
Project documents
Frequently changing business data
You could train or fine-tune a model on some of this information, but that can be expensive and does not solve the problem of constantly changing information.
This is where RAG — Retrieval-Augmented Generation becomes extremely useful.
RAG allows an AI application to:
Receive a user's question.
Search an external knowledge source.
Retrieve the most relevant information.
Give that information to an LLM.
Generate an answer grounded in the retrieved information.
In simple terms:
RAG = Search for relevant knowledge + Give it to the LLM + Generate an answer
1. What is RAG?
RAG stands for:
Retrieval-Augmented Generation
It combines two major capabilities:
Retrieval
Find relevant information from an external knowledge source.
Generation
Use an LLM to generate a natural-language answer using that retrieved information.
A simplified representation is:
User Question
|
v
Retriever
|
v
Relevant Documents
|
v
LLM / GPT
|
v
Generated Answer
For example:
User asks:
"What is our company's leave policy for employees with more than 5 years of service?"
The LLM itself may not know your company's policy.
A RAG system searches your company's HR documents, finds the relevant policy, and passes it to the LLM.
The LLM then answers:
"According to the company's leave policy, employees with more than five years of service are eligible for ..."
The important part is that the answer is based on your organization's data.
2. Who Invented RAG?
RAG was not created as a commercial product by a single company.
The term and a well-known formal RAG architecture were introduced in the research paper:
"Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks"
published in 2020.
The paper was authored by:
Patrick Lewis
Ethan Perez
Aleksandra Piktus
Fabio Petroni
Vladimir Karpukhin
Naman Goyal
Heinrich Küttler
Mike Lewis
Wen-tau Yih
Tim Rocktäschel
Sebastian Riedel
Douwe Kiela
The paper described RAG models that combine a pretrained sequence-to-sequence model with a dense vector index used as external non-parametric memory.
The original work was associated with the Facebook AI Research ecosystem, now part of Meta AI.
However, it is important to understand that the broader idea of retrieving external knowledge and combining it with language models existed before the 2020 RAG paper. For example, Google's REALM research also explored retrieval-augmented language modeling in 2020.
Therefore:
RAG is a research architecture/pattern, not a programming language or a single software product.
3. In Which Programming Language Was RAG Developed?
This is one of the most common misconceptions.
RAG is not a programming language.
It is an AI architecture/pattern.
You can implement RAG using many programming languages.
Common choices include:
| Language | Typical Usage |
|---|---|
| Python | AI/ML, RAG experimentation, LangChain, LlamaIndex |
| C# | Enterprise .NET applications |
| Java | Enterprise applications |
| JavaScript/TypeScript | Node.js applications |
| Go | High-performance backend services |
| C++ | High-performance AI infrastructure |
Python is particularly popular in AI research because of its extensive machine-learning ecosystem.
But a company building an enterprise application using:
ASP.NET Core
Angular
Azure
SQL Server
can implement RAG using C#/.NET.
4. Why Was RAG Needed?
Traditional LLM architecture looks like this:
User
|
v
LLM
|
v
Answer
The model relies primarily on knowledge encoded in its parameters.
This creates several problems.
Problem 1 — Private Data
Suppose your company has:
EmployeePolicy.pdf
ProductManual.pdf
CustomerSupport.pdf
Architecture.docx
ProjectDocumentation.pdf
The public LLM does not automatically know these documents.
Problem 2 — Frequently Changing Data
Imagine asking:
"What is today's product inventory?"
The answer may change every hour.
You don't want to retrain an LLM every time inventory changes.
Problem 3 — Hallucination
An LLM can sometimes generate information that sounds convincing but is incorrect.
RAG can reduce this risk by supplying relevant source information to the model.
However:
RAG does not completely eliminate hallucinations.
Research continues to show that insufficient or poor-quality retrieved context can still cause incorrect answers.
5. Main Purpose of RAG
The primary purpose of RAG is:
To allow an LLM to use external, relevant and potentially up-to-date knowledge while generating an answer.
This provides several benefits:
1. Access private information
Example:
Company HR Documents
Company Technical Documents
Company Product Documents
2. Access frequently changing information
Example:
Inventory
Prices
Policies
News
Tickets
Orders
3. Reduce hallucination
The model can use retrieved evidence instead of relying entirely on its internal knowledge.
4. Provide source references
A well-designed RAG application can show:
Source:
Employee_Leave_Policy.pdf
Page 12
5. Avoid retraining for every document update
Instead of retraining the LLM whenever a document changes:
Update Document
|
v
Update Knowledge Index
|
v
RAG uses new information
6. RAG Architecture
A typical RAG system looks like this:
┌──────────────────┐
│ User │
└────────┬─────────┘
|
v
┌──────────────────┐
│ User Question │
└────────┬─────────┘
|
v
┌──────────────────┐
│ Query Processing │
└────────┬─────────┘
|
v
┌──────────────────┐
│ Embedding Model │
└────────┬─────────┘
|
v
┌──────────────────┐
│ Vector Database │
└────────┬─────────┘
|
v
┌──────────────────┐
│ Relevant Chunks │
└────────┬─────────┘
|
v
┌──────────────────┐
│ Prompt + Context │
└────────┬─────────┘
|
v
┌──────────────────┐
│ LLM │
└────────┬─────────┘
|
v
┌──────────────────┐
│ Final Answer │
└──────────────────┘
7. The Two Major Parts of RAG
A RAG system generally has two major workflows:
A. Data Ingestion
This happens before users ask questions.
Documents
|
v
Document Extraction
|
v
Chunking
|
v
Embeddings
|
v
Vector Database
B. Query Processing
This happens when the user asks a question.
User Question
|
v
Embedding
|
v
Vector Search
|
v
Relevant Chunks
|
v
LLM
|
v
Answer
Understanding these two pipelines is essential for understanding RAG.
8. What Is an Embedding?
An embedding converts text into a numerical representation called a vector.
For example:
"How can I reset my password?"
might be represented conceptually as:
[0.21, -0.45, 0.73, 0.11, ...]
Real embedding vectors can contain hundreds or thousands of dimensions depending on the embedding model.
The important concept is:
Similar meanings produce vectors that are close to each other in vector space.
For example:
"How do I change my password?"
and:
"What is the procedure for resetting my password?"
have different words but similar meaning.
Their embeddings should therefore be semantically similar.
9. Why Do We Need a Vector Database?
Suppose you have:
1,000 documents
10,000 documents
1 million documents
Searching all the text directly for every question can become inefficient.
A vector database stores embeddings and allows similarity searches.
Common technologies include:
Azure AI Search
PostgreSQL with pgvector
Elasticsearch
OpenSearch
Pinecone
Weaviate
Milvus
Qdrant
Chroma
Redis with vector search capabilities
The exact technology depends on your architecture and requirements.
10. What Is a Vector Database?
A vector database stores data such as:
Document ID
Chunk ID
Text
Embedding
Metadata
Example:
DocumentId:
EMP001
ChunkId:
EMP001-CHUNK-12
Text:
Employees are eligible for 20 days of annual leave...
Embedding:
[0.21, 0.52, -0.11, ...]
Metadata:
Department = HR
DocumentType = Policy
Year = 2026
11. Document Chunking
One of the most important steps in RAG is chunking.
Suppose a PDF contains 100 pages.
You generally should not send the entire PDF to the LLM for every question.
Instead, divide it into smaller pieces.
For example:
Document
|
+---- Chunk 1
|
+---- Chunk 2
|
+---- Chunk 3
|
+---- Chunk 4
|
+---- Chunk 5
A chunk might contain:
500–1000 tokens
The exact size should be determined experimentally based on the document type and retrieval quality.
12. Chunk Overlap
Sometimes important information crosses chunk boundaries.
For example:
Chunk 1:
Employees are eligible for annual leave after completing...
Chunk 2:
...one year of continuous service.
If there is no overlap, retrieval may lose context.
Therefore, systems may use overlapping chunks.
Example:
Chunk 1
---------------------
A B C D E F G H
Chunk 2
E F G H I J K L
The overlap improves the chance that related information remains together.
13. Metadata
Metadata is extremely important in enterprise RAG.
Example:
{
"documentId": "HR-2026-001",
"department": "HR",
"documentType": "LeavePolicy",
"year": 2026,
"region": "India"
}
Metadata allows filtering.
For example:
"Search only HR documents from 2026."
Instead of searching the entire knowledge base:
Vector Search
+
Department = HR
+
Year = 2026
This is called metadata filtering.
14. End-to-End RAG Pipeline
Let's understand the complete process.
Step 1 — Upload Documents
Example:
HRPolicy.pdf
Step 2 — Extract Text
The system extracts text from:
PDF
DOCX
TXT
HTML
Web pages
Database
For scanned documents, OCR may be required.
Step 3 — Chunk the Text
Example:
HRPolicy.pdf
|
+-- Chunk 1
+-- Chunk 2
+-- Chunk 3
+-- Chunk 4
Step 4 — Generate Embeddings
Each chunk is converted into a vector.
Chunk 1
|
Embedding Model
|
Vector
Step 5 — Store in Vector Database
Vector
+
Text
+
Metadata
is stored.
15. Query-Time Process
Now the user asks:
"How many annual leave days can I take?"
The system performs:
Question
|
v
Embedding
|
v
Vector Search
|
v
Top Relevant Chunks
Suppose the database returns:
Chunk 12
Chunk 27
Chunk 31
These are passed to the LLM.
16. Prompt Augmentation
The application creates a prompt similar to:
You are an HR assistant.
Answer the question using only the provided context.
Context:
--------------------
Employees are entitled to 20 days
of annual leave per calendar year.
Leave must be requested through
the employee portal.
Question:
How many annual leave days can I take?
The LLM then generates:
Employees are entitled to 20 days
of annual leave per calendar year.
This is the generation part of RAG.
17. RAG vs Traditional LLM
| Feature | Traditional LLM | RAG |
|---|---|---|
| General knowledge | Yes | Yes |
| Private company data | Limited | Yes |
| Dynamic data | Limited | Yes |
| External documents | Not automatically | Yes |
| Knowledge updates | Model-dependent | Update knowledge source/index |
| Source citations | Not guaranteed | Can be implemented |
| Hallucination risk | Exists | Can be reduced |
| Retraining required for every document | No/depends | Usually no |
| Enterprise knowledge assistant | Limited | Excellent fit |
18. RAG vs Fine-Tuning
This is one of the most important concepts.
Fine-Tuning
Fine-tuning changes model behavior/weights using training examples.
Useful for:
Style
Behavior
Task specialization
Output format
Domain-specific behavior
RAG
RAG provides external knowledge at query time.
Useful for:
Private documents
Current information
Frequently changing information
Knowledge bases
Company policies
Product documentation
A simple rule:
Use RAG to give the model knowledge.
Use fine-tuning to change how the model behaves.
Sometimes enterprises use both.
19. Real-Time Example #1 — Company HR Assistant
Imagine a company has:
Employee Handbook
Leave Policy
Travel Policy
Insurance Policy
Work From Home Policy
Salary Policy
An employee asks:
"How many work-from-home days can I take?"
RAG:
User
|
v
Question
|
v
Embedding
|
v
Vector Search
|
v
HR Documents
|
v
Relevant Policy
|
v
LLM
|
v
Answer
The employee doesn't need to manually search hundreds of pages.
20. Real-Time Example #2 — Customer Support
Suppose an organization sells networking equipment.
Documents:
Router Manual
Switch Manual
Troubleshooting Guide
Warranty Policy
Installation Guide
Customer asks:
"My router is showing a red status light. What should I check?"
RAG retrieves the troubleshooting section.
The LLM generates a user-friendly answer based on the retrieved manual.
This is much better than asking the model to guess the troubleshooting procedure.
21. Real-Time Example #3 — Banking
A bank may have:
Loan Policy
Credit Card Policy
Interest Rate Policy
KYC Documentation
Account Rules
Product Terms
A customer asks:
"What documents are required for this loan?"
RAG retrieves the relevant policy.
The LLM summarizes it.
The application can also provide:
Source Document
Section
Page
Last Updated Date
This is particularly valuable for regulated environments.
22. Real-Time Example #4 — Software Development
Suppose your organization has:
Architecture Documents
API Documentation
Coding Standards
Database Documentation
Microservice Documentation
Deployment Documentation
A developer asks:
"How does the Customer Service communicate with the Order Service?"
RAG searches the architecture documentation.
It may retrieve:
Customer Service
|
v
Azure Service Bus
|
v
Order Service
The LLM can then explain the architecture.
23. Real-Time Example #5 — E-Commerce
Suppose an online store has:
Products
Prices
Inventory
Returns
Shipping Policies
Customer Orders
Customer asks:
"Can I return my order?"
RAG can retrieve the applicable return policy.
For dynamic information such as order status, the RAG application may also retrieve information directly from operational APIs or databases.
This leads to an important architecture:
LLM
|
+---- Vector Search
|
+---- SQL Database
|
+---- REST API
|
+---- Business Services
This is often more powerful than document-only RAG.
24. RAG With SQL Database
RAG does not mean everything has to be stored in a vector database.
Suppose you ask:
"How many orders did customer 10025 place last month?"
A vector database is not necessarily the right tool.
A better architecture may be:
User Question
|
v
Intent Detection
|
+------------------+
| |
v v
Document Search SQL Query
| |
+--------+---------+
|
v
LLM
|
v
Answer
This is often called a hybrid/agentic architecture.
25. RAG + SQL Example
Question:
"What is our return policy?"
Use:
Vector Search
Question:
"How many orders were placed yesterday?"
Use:
SQL
Question:
"Why was customer 12345's order delayed?"
Potentially use:
SQL
+
Order API
+
Support tickets
+
RAG
The LLM can orchestrate the sources.
26. Semantic Search vs Keyword Search
Traditional search might search:
"password reset"
and look for exact words.
Semantic search understands meaning.
Question:
"I forgot my login credentials. How can I get back into my account?"
It can retrieve:
Password Reset Procedure
even though the exact phrase may not appear.
This is one of the major benefits of embeddings.
27. Hybrid Search
Modern enterprise RAG systems often combine:
Keyword Search
+
Vector Search
For example:
BM25 / keyword search
+
Semantic vector search
|
v
Combined Results
Why?
Keyword search is excellent for exact identifiers such as:
INV-2026-00125
Customer ID 10045
Error E5001
API-123
Vector search is excellent for semantic meaning.
Combining both can improve retrieval quality.
28. Reranking
Retrieving the top 20 documents does not necessarily mean all 20 are equally relevant.
A reranker can evaluate them again.
Query
|
v
Retriever
|
v
Top 20 documents
|
v
Reranker
|
v
Top 5 documents
|
v
LLM
This can improve the quality of the context provided to the LLM.
29. Basic RAG Architecture for .NET
For your .NET ecosystem, an enterprise architecture could look like:
Angular
|
v
ASP.NET Core
|
+---------+---------+
| |
v v
RAG Service Business APIs
|
v
Embedding Service
|
v
Azure AI Search
|
v
Enterprise Documents
|
v
LLM
Possible Azure components include:
Angular
|
Azure App Service / Static Web Apps
|
ASP.NET Core Web API
|
Azure AI Search
|
Azure OpenAI
|
Blob Storage
|
SQL Server / Azure SQL
The exact Azure services can vary depending on the application.
30. Simple C# RAG Flow
Conceptually:
public async Task<string> AskAsync(string question)
{
var queryEmbedding =
await embeddingService.CreateEmbeddingAsync(question);
var documents =
await vectorStore.SearchAsync(queryEmbedding, topK: 5);
var context = string.Join(
"\n\n",
documents.Select(x => x.Content));
var prompt = $"""
Answer the question using only the context below.
Context:
{context}
Question:
{question}
""";
return await llm.GenerateAsync(prompt);
}
The important flow is:
Question
↓
Embedding
↓
Vector Search
↓
Relevant Documents
↓
Prompt
↓
LLM
↓
Answer
31. Document Ingestion in C#
Conceptually:
public async Task IndexDocumentAsync(Document document)
{
var chunks = ChunkDocument(document.Content);
foreach (var chunk in chunks)
{
var embedding =
await embeddingService.CreateEmbeddingAsync(chunk);
await vectorStore.AddAsync(new VectorDocument
{
DocumentId = document.Id,
Content = chunk,
Embedding = embedding,
Metadata = document.Metadata
});
}
}
This creates the knowledge base.
32. RAG Application Using Angular + .NET
A practical enterprise solution could be:
Angular
|
|
HTTP / HTTPS
|
v
ASP.NET Core Web API
|
+-------+-------+
| |
v v
RAG Service Auth Service
|
+-----+------+
| |
v v
Embedding Vector DB
Service
|
v
LLM
Angular provides:
Chat UI
Document Upload
Source Display
Conversation History
ASP.NET Core provides:
Authentication
Authorization
RAG orchestration
Document processing
Business logic
API endpoints
Logging
33. Example API
A simple API could be:
POST /api/rag/ask
Request:
{
"question": "What is the leave policy?"
}
Response:
{
"answer": "Employees are eligible for annual leave...",
"sources": [
{
"document": "LeavePolicy.pdf",
"page": 12
}
]
}
Angular can display:
Answer
--------------------------------
Employees are eligible for...
Sources
--------------------------------
LeavePolicy.pdf
Page 12
34. RAG Security
Enterprise RAG must take security seriously.
Suppose:
Employee A
should not access:
Employee B's salary information.
Simply storing everything in one vector index can create security problems.
The system should enforce:
User
|
v
Authentication
|
v
Authorization
|
v
Security Filter
|
v
Retrieval
Metadata can help:
{
"department": "Finance",
"classification": "Confidential",
"allowedRoles": [
"FinanceManager"
]
}
The retrieval layer should apply appropriate authorization filters before returning context.
35. RAG and JWT Authentication
In an ASP.NET Core enterprise application:
Angular
|
v
JWT
|
v
ASP.NET Core
|
v
User Claims
|
v
RAG Authorization
|
v
Filtered Retrieval
For example:
Role = HRManager
Department = HR
could result in:
Department = HR
AND
UserAuthorized = true
during retrieval.
36. RAG and Microservices
RAG fits naturally into microservice architecture.
Example:
API Gateway
|
+--------------+--------------+
| | |
v v v
User Service Order Service RAG Service
|
+----------+----------+
| |
v v
Vector Search LLM
|
v
Document Store
A dedicated RAG service can own:
Document ingestion
Chunking
Embedding
Retrieval
Reranking
Prompt construction
LLM interaction
Citation generation
37. RAG + Azure Service Bus
For large enterprise applications, document processing should not always happen synchronously.
For example:
User uploads PDF
|
v
Blob Storage
|
v
Azure Service Bus
|
v
Document Processing Service
|
v
Text Extraction
|
v
Chunking
|
v
Embedding
|
v
Azure AI Search
This provides an asynchronous ingestion pipeline.
38. RAG + Blob Storage
A common Azure architecture:
Blob Storage
|
v
Document Processor
|
Chunking
|
Embeddings
|
v
Azure AI Search
|
v
RAG API
|
v
Azure OpenAI
Documents can remain in Blob Storage while searchable chunks and metadata are stored in the search system.
39. RAG Evaluation
Building a RAG application is not just about making it work.
You need to measure it.
Important metrics include:
Retrieval Precision
Did the system retrieve relevant documents?
Retrieval Recall
Did it retrieve the information needed to answer the question?
Faithfulness
Does the generated answer actually follow the retrieved context?
Answer Relevance
Does the answer address the user's question?
Latency
How long does the complete request take?
Cost
How many embedding and LLM tokens are being consumed?
40. Common RAG Problems
Problem 1 — Bad Chunking
If chunks are too large:
Too much irrelevant context
If chunks are too small:
Important context may be lost
Problem 2 — Poor Retrieval
If the retriever returns irrelevant documents:
Wrong Context
↓
LLM
↓
Poor Answer
Problem 3 — Hallucination
Even with RAG, an LLM may generate information not supported by the context.
Therefore prompts should clearly instruct the model:
Use the provided context.
If the answer cannot be found in
the context, say that the information
is not available.
Problem 4 — Too Much Context
Sending hundreds of irrelevant chunks to the LLM can:
Increase cost
Increase latency
Reduce answer quality
Therefore retrieval and reranking are important.
41. Advanced RAG
Basic RAG:
Question
↓
Vector Search
↓
LLM
Advanced RAG can look like:
User Question
|
v
Query Rewriting
|
v
Hybrid Retrieval
|
v
Metadata Filtering
|
v
Reranking
|
v
Context Compression
|
v
LLM
|
v
Citation / Validation
|
v
Answer
42. Agentic RAG
A newer approach is Agentic RAG.
Instead of performing one retrieval operation, an AI agent can determine what information it needs and use multiple tools.
For example:
User:
"Why was customer 10025's order delayed?"
The agent may decide to query:
Customer API
+
Order Database
+
Support Tickets
+
Product Documentation
Then combine the results.
A modern Agentic RAG system may therefore contain:
AI Agent
|
+---------+---------+
| | |
v v v
SQL Search API
| | |
+---------+---------+
|
v
LLM
Recent industry research is increasingly exploring agentic RAG architectures that use planning, routing and retrieval to improve grounded responses.
43. RAG vs Agentic RAG
| Feature | Basic RAG | Agentic RAG |
|---|---|---|
| Retrieval | Usually fixed | Dynamic |
| Decision making | Limited | Agent-driven |
| Multiple tools | Limited | Yes |
| SQL/API integration | Possible | Strong |
| Query planning | Limited | Yes |
| Complexity | Lower | Higher |
| Cost | Lower | Potentially higher |
| Enterprise workflows | Good | Excellent for complex workflows |
44. RAG Security Architecture
For enterprise applications:
User
|
v
Authentication
|
v
Authorization
|
v
RAG API
|
Security Filter
|
v
Retrieval
|
v
LLM
|
v
Output Validation
|
v
User
Important controls include:
Authentication
Authorization
Tenant isolation
Document-level permissions
Metadata filtering
Encryption
Audit logging
PII protection
Prompt-injection defenses
Output validation
45. Multi-Tenant RAG
Consider a SaaS application with:
Company A
Company B
Company C
The system must prevent:
Company A → Company B documents
A common approach is to include:
TenantId
in document metadata.
Example:
{
"tenantId": "COMPANY-A",
"documentId": "DOC-1001"
}
At query time:
WHERE TenantId = CurrentUser.TenantId
This is critical for enterprise SaaS systems.
46. RAG Cost Optimization
RAG does not automatically mean low cost.
Costs may come from:
Embedding generation
Vector search
LLM input tokens
LLM output tokens
Storage
Document processing
OCR
Reranking
Optimization techniques include:
1. Good chunking
Avoid unnecessary context.
2. Top-K optimization
Don't retrieve 100 chunks when 5 are enough.
3. Reranking
Retrieve candidates first, then select the best ones.
4. Cache embeddings
Don't regenerate embeddings unnecessarily.
5. Cache frequent questions
For repeated queries, response caching may help.
47. RAG Complete Architecture for an Enterprise .NET Application
A production architecture could look like:
Angular
|
v
API Management
|
v
ASP.NET Core API
|
+-------+-------+
| |
v v
RAG Service Business APIs
|
+----------+----------+
| | |
v v v
Embedding Search SQL/API
Service Index
| |
| v
| Vector Search
| |
+----------+
|
v
LLM
|
v
Answer + Sources
Document ingestion:
User/Admin
|
v
Blob Storage
|
v
Service Bus
|
v
Document Processor
|
v
Text Extraction
|
v
Chunking
|
v
Embedding
|
v
Search Index
48. Recommended Technology Stack for a .NET Developer
For an enterprise .NET developer, one possible stack is:
| Layer | Technology |
|---|---|
| Frontend | Angular |
| Backend | ASP.NET Core |
| Authentication | Microsoft Entra ID / OAuth/OIDC |
| LLM | Azure OpenAI or another LLM provider |
| Search | Azure AI Search |
| Document Storage | Azure Blob Storage |
| Database | Azure SQL |
| Messaging | Azure Service Bus |
| Monitoring | Application Insights / Azure Monitor |
| Secrets | Azure Key Vault |
| API Gateway | Azure API Management |
| Containerization | Docker |
| Orchestration | AKS |
| CI/CD | Azure DevOps |
| Embeddings | Embedding model |
| Vector Store | Azure AI Search / another vector-capable store |
This is particularly suitable for organizations already invested in Microsoft technologies.
49. RAG Request Flow
Let's follow one request from beginning to end.
User asks:
"What is our refund policy?"
Step 1
Angular sends:
POST /api/rag/ask
Step 2
ASP.NET Core authenticates the user.
Step 3
RAG service creates an embedding.
Step 4
Search system performs semantic/hybrid search.
Step 5
Relevant documents are retrieved.
RefundPolicy.pdf
Section 4
Section 7
Step 6
Reranker selects the best chunks.
Step 7
Application constructs the prompt.
Step 8
LLM generates the answer.
Step 9
Application returns:
{
"answer": "Customers can request a refund within...",
"sources": [
{
"document": "RefundPolicy.pdf",
"section": "4"
}
]
}
Step 10
Angular displays the answer and sources.
50. Is RAG a Replacement for an LLM?
No.
RAG normally works with an LLM.
Think of the responsibilities like this:
Vector Search
|
| Finds information
v
Relevant Context
|
| Gives context
v
LLM
|
| Understands and generates
v
Human-readable Answer
The vector database does not replace the LLM.
The LLM does not replace the search engine.
They work together.
51. Is RAG the Same as ChatGPT?
No.
ChatGPT is an AI application/service that can use LLMs and various tools.
RAG is an architectural pattern.
A developer can build:
Custom RAG Application
using:
ASP.NET Core
+
Vector Database
+
Embedding Model
+
LLM
52. When Should You Use RAG?
RAG is a strong choice when:
You have private documents
OR
You have frequently changing information
OR
You need source-grounded answers
OR
You need enterprise knowledge search
OR
You need natural-language access to documentation
Examples:
HR Assistant
Customer Support Assistant
Legal Document Search
Technical Documentation Assistant
Product Assistant
Knowledge Management
Research Assistant
Enterprise Search
53. When RAG May Not Be the Best Solution
Don't automatically use RAG for everything.
For example:
"Calculate 125 × 75."
You don't need document retrieval.
Similarly:
"Sort this list."
RAG isn't necessary.
For structured business data:
SQL
API
Business Service
may be more appropriate.
The best enterprise architecture often combines:
LLM
+
RAG
+
SQL
+
APIs
+
Business Services
+
Tools
54. RAG vs Search Engine
Traditional search:
Question
|
v
Search Engine
|
v
10 Results
RAG:
Question
|
v
Search
|
v
Relevant Documents
|
v
LLM
|
v
Natural Language Answer
Traditional search gives you documents.
RAG can understand the retrieved information and synthesize an answer.
55. RAG vs Database
A database is designed primarily for structured data.
For example:
CustomerId
Name
OrderId
OrderDate
Amount
RAG is especially useful for unstructured/semi-structured knowledge such as:
PDFs
Manuals
Policies
Documentation
Emails
Knowledge articles
Text
Enterprise systems often use both.
56. Golden Rule for RAG
A very useful way to remember RAG is:
LLM = Brain
Embedding Model = Meaning Representation
Vector Database = Memory/Search
Retriever = Finds Memory
Prompt = Instructions
RAG = Brain + External Memory
This is only an analogy, but it makes the architecture easier to understand.
57. The Future of RAG
RAG is evolving beyond simple:
Search → Prompt → LLM
Modern systems increasingly explore:
Query Planning
+
Hybrid Search
+
Reranking
+
Knowledge Graphs
+
Multimodal Retrieval
+
Agentic Workflows
+
Tool Calling
+
SQL
+
APIs
+
Validation
Research is also exploring techniques for improving RAG efficiency and answer quality, including systems that use multiple retrieved document subsets and verification stages.
58. Complete RAG Mental Model
Remember this architecture:
┌─────────────────┐
│ USER │
└────────┬────────┘
|
v
┌─────────────────┐
│ QUESTION │
└────────┬────────┘
|
v
┌─────────────────┐
│ QUERY PROCESSOR │
└────────┬────────┘
|
v
┌─────────────────┐
│ EMBEDDING │
└────────┬────────┘
|
v
┌───────────────────────┐
│ VECTOR / HYBRID SEARCH│
└───────────┬───────────┘
|
v
┌─────────────────┐
│ RERANKER │
└────────┬────────┘
|
v
┌─────────────────┐
│ RELEVANT CONTEXT│
└────────┬────────┘
|
v
┌─────────────────┐
│ LLM │
└────────┬────────┘
|
v
┌─────────────────┐
│ ANSWER + SOURCE │
└─────────────────┘
59. Key Takeaways
The most important points are:
RAG stands for Retrieval-Augmented Generation.
RAG is an architecture/pattern, not a programming language.
The well-known RAG architecture was introduced in a 2020 research paper by Patrick Lewis and collaborators.
RAG combines:
Retrieval + LLM GenerationEmbeddings convert text into vectors representing semantic meaning.
Vector databases/search engines allow semantic retrieval.
Documents should normally be split into chunks.
Metadata is extremely important for filtering and security.
RAG can use:
PDFs DOCX Websites Databases APIs Knowledge BasesRAG can reduce hallucination but cannot guarantee that every answer is correct.
Fine-tuning and RAG solve different problems.
RAG is highly useful for enterprise applications.
.NET developers can build RAG applications using:
Angular
+
ASP.NET Core
+
Embedding Model
+
Vector Search
+
LLM
+
SQL
+
Azure Services
Advanced RAG can include:
Hybrid Search
Reranking
Metadata Filtering
Query Rewriting
Context Compression
Agentic Workflows
SQL
APIs
Knowledge Graphs
60. Final Conclusion
RAG is one of the most important architectural patterns for building practical enterprise generative-AI applications.
An LLM provides the reasoning and language-generation capability, while RAG provides a mechanism for accessing external knowledge.
The basic concept is simple:
Traditional LLM
Question ──────────> LLM ──────────> Answer
RAG Application
Question
|
v
Retrieve Knowledge
|
v
Relevant Context
|
v
LLM
|
v
Grounded Answer
The real power appears when RAG is combined with enterprise technologies:
Angular
+
ASP.NET Core
+
Azure OpenAI
+
Azure AI Search
+
Azure SQL
+
Blob Storage
+
Azure Service Bus
+
API Management
+
Key Vault
+
Application Insights
+
Docker / AKS
+
Azure DevOps
This combination allows developers to build practical systems such as:
Enterprise Knowledge Assistant
Customer Support AI
HR Assistant
Technical Documentation Assistant
Product Assistant
Financial Document Assistant
Legal Document Search
Developer Copilot
Enterprise Search
The key idea to remember is:
RAG does not teach an LLM everything permanently. Instead, it retrieves the right information at the right time and gives that information to the LLM so it can generate a more relevant, grounded response.
That makes RAG one of the most useful bridges between traditional enterprise data and modern Generative AI.
References
The original RAG research paper is available on arXiv: Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks.
Google's research on REALM provides useful background on retrieval-augmented language modeling and explicit external knowledge retrieval.
