Retrieval-Augmented Generation (RAG): From Basics to Advanced Techniques

Large Language Models (LLMs) can generate impressive answers, write code, summarize documents, and explain complex topics. But they have an important limitation: they do not automatically know everything happening inside your organization, your private database, or the latest documents available today.

This is where Retrieval-Augmented Generation (RAG) becomes useful.

RAG allows an LLM to retrieve relevant information from an external knowledge source and use that information while generating an answer.

Instead of asking an LLM to answer only from what it learned during training, RAG gives it access to a separate knowledge source at query time.

A simple way to think about it is:

LLM + External Knowledge + Retrieval = RAG

RAG has evolved significantly since the original 2020 architecture. Modern systems can use vector search, keyword search, hybrid retrieval, reranking, query rewriting, GraphRAG, multimodal retrieval, corrective retrieval, and even agentic workflows.

A 2025 systematic literature review examined a large body of RAG research and highlighted how the field has expanded across retrieval methods, vector databases, chunking strategies, evaluation metrics, multimodal systems, graph-based retrieval, and iterative retrieval-generation approaches.

In this guide, we will start with the basics and gradually move toward advanced RAG.

What Is RAG?

Retrieval-Augmented Generation is an architecture that connects a language model with an external information source.

The three words explain the idea:

• Retrieval → Find relevant information.

• Augmented → Add that information to the model's context.

• Generation → Generate an answer using the retrieved information.

Imagine you build a chatbot for a company.

The company has:

• HR policies

• Product documentation

• Customer-support manuals

• Internal FAQs

• Technical documentation

• Training materials

• PDF files

• Database records

You could try to train an LLM on all this information.

But that creates several problems.

Every time a document changes, the model would need some way to receive the new information. Training can also be expensive and does not naturally provide a simple mechanism for controlling which private documents a particular user can access.

With RAG, you can keep the knowledge outside the model.

When someone asks:

"What is our refund policy for enterprise customers?"

the system searches the company's knowledge base, retrieves relevant sections, and provides those sections to the LLM.

The LLM then generates the response using that retrieved context.

This general architecture is also described in the literature as combining a retriever with a generator so that external information can supplement the model's learned knowledge.

Simple example

Without RAG:

User → LLM → Answer

With RAG:

User → Retrieval System → Relevant Documents → LLM → Answer

That additional retrieval step is what makes RAG powerful.

Why Do We Need RAG?

To understand RAG, we first need to understand some limitations of LLMs.

1. LLM knowledge can become outdated

An LLM is trained on a particular dataset and training period.

Suppose a company changes its vacation policy today.

A general-purpose LLM will not automatically know about that change.

RAG can retrieve the updated policy from the company's document repository.

2. LLMs may hallucinate

A hallucination happens when an AI system produces information that sounds convincing but is unsupported or incorrect.

For example:

User:
"What is the company's 2026 refund policy?"

Without access to the company's actual policy, the model may generate a plausible answer.

With RAG, the system can retrieve the relevant policy document and provide it as context.

RAG does not automatically eliminate hallucinations. Retrieval quality, document quality, prompt design, model behavior, and evaluation all still matter.

Research on RAG consistently identifies reliability and factual grounding as important challenges.

3. Private company information is not normally part of a public model

Imagine an organization has thousands of internal documents.

A general LLM does not automatically have access to:

• Internal databases

• Employee handbooks

• Private product documentation

• Internal engineering tickets

• Customer records

• Company procedures

RAG provides a mechanism for connecting those sources to an LLM.

4. Fine-tuning is not the same as adding knowledge

This is an important distinction.

RAG is primarily about giving the model access to external information.

Fine-tuning changes model behavior by training the model further on examples.

For example:

RAG can help an AI assistant answer questions using the latest company documentation.

Fine-tuning can help a model follow a particular style, format, or task pattern.

The two approaches can also be combined.

RAG vs Traditional LLM

Let's compare the basic approaches.

RAG therefore acts as a bridge between an LLM and external information.

How Does RAG Work?

A typical RAG pipeline contains several stages.

Basic workflow

Documents → Chunking → Embeddings → Vector Database → User Query → Retrieval → Context → LLM → Answer

Let's understand every step.

Step 1: Collect Your Data

First, you need a knowledge source.

This could include:

• PDF documents

• Web pages

• Word documents

• Markdown files

• Product documentation

• Database records

• Support tickets

• Research papers

• Internal company documents

• Code repositories

This stage is sometimes called data ingestion.

The quality of this data matters enormously.

If your knowledge base contains outdated, duplicated, incomplete or incorrect information, your RAG system can retrieve that information and produce a poor answer.

A useful principle is:

Bad data → Bad retrieval → Bad context → Bad answer

Step 2: Split Documents Into Chunks

Large documents are usually divided into smaller pieces called chunks.

Why?

Suppose you have a 200-page technical manual.

You probably do not want to send the entire manual to the LLM for every question.

Instead, you divide it into meaningful sections.

For example:

Document
│
├── Chapter 1
│   ├── Chunk 1
│   ├── Chunk 2
│   └── Chunk 3
│
├── Chapter 2
│   ├── Chunk 4
│   ├── Chunk 5
│   └── Chunk 6
│
└── Chapter 3
    ├── Chunk 7
    └── Chunk 8

The goal is to create chunks that are small enough for accurate retrieval but large enough to preserve meaning.

Research surveys identify fixed-length, sentence-level, sliding-window, hierarchical and other chunking strategies as common approaches.

Common Chunking Strategies

Fixed-size chunking

Split documents according to a fixed number of characters or tokens.

Example:

Chunk size = 500 tokens
Overlap = 50 tokens

This is simple and easy to implement.

Sentence-based chunking

Instead of cutting text at arbitrary positions, the system groups complete sentences.

This can preserve semantic meaning better.

Paragraph-based chunking

Documents are split according to paragraphs.

This works well when the original document structure is meaningful.

Recursive chunking

The system tries different separators hierarchically.

For example:

Document
↓
Sections
↓
Paragraphs
↓
Sentences
↓
Smaller chunks

This can preserve document structure better than simply cutting every N characters.

Semantic chunking

Semantic chunking attempts to identify sections that are conceptually related.

For example, instead of:

Chunk 1: half of an explanation
Chunk 2: other half of the explanation

the system tries to create:

Chunk 1: Complete explanation of authentication
Chunk 2: Complete explanation of authorization

This can improve retrieval because each chunk represents a more meaningful concept.

Step 3: Create Embeddings

After documents are divided into chunks, each chunk can be converted into a numerical representation called an embedding.

An embedding represents the semantic meaning of text as a vector.

For example:

"How do I reset my password?"

might become something conceptually like:

[0.21, -0.14, 0.72, 0.09, ...]

The actual vector contains many dimensions.

The important idea is that semantically related text can have similar representations.

For example:

"How can I change my password?"

and

"I forgot my password. How do I reset it?"

use different words but have similar meanings.

Semantic embeddings can help the retrieval system recognize that relationship.

Step 4: Store Embeddings

The embeddings are stored in a system capable of similarity search.

This is commonly a vector database or vector search system.

Examples include:

• FAISS

• Pinecone

• Weaviate

• Qdrant

• Chroma

• Milvus

• PostgreSQL with vector extensions

A systematic review notes that vector infrastructure commonly uses approximate nearest-neighbor techniques such as HNSW and FAISS-style indexes to support large-scale similarity search, while managed vector services can simplify deployment but introduce operational and cost considerations.

Step 5: Convert the User Query Into an Embedding

Now the user asks a question.

For example:

"How can I reset my company password?"

The query is also converted into an embedding.

The system then compares the query vector against stored document vectors.

The objective is to find the chunks that are most relevant to the question.

Step 6: Retrieve Relevant Information

Suppose the database contains:

Chunk 1:
Company password policy

Chunk 2:
Laptop security policy

Chunk 3:
Password reset instructions

Chunk 4:
Employee vacation policy

The user's question is:

"How can I reset my company password?"

The retrieval system should identify:

Chunk 3

as highly relevant.

It may retrieve several chunks rather than only one.

For example:

Top 5 results
↓
Chunk 3
Chunk 1
Chunk 8
Chunk 12
Chunk 17

These results are then passed into the generation stage.

Step 7: Add Retrieved Context to the Prompt

The LLM receives something like:

User Question:
How can I reset my company password?

Relevant Context:
[Retrieved document 1]

[Retrieved document 2]

[Retrieved document 3]

Instructions:
Answer the user's question using the provided context.

The model then generates the final response.

This is the generation stage.

Step 8: Generate the Final Answer

The LLM uses:

1. User question

2. Retrieved information

3. System instructions

4. Its language-generation capabilities

to produce the final answer.

The result might be:

"You can reset your company password from the employee portal. Select 'Forgot Password', verify your identity, and follow the reset instructions."

The important difference is that the answer is grounded using information retrieved from the organization's knowledge base.

The Complete RAG Pipeline

Putting everything together:

                    OFFLINE / INDEXING
                           
Documents
    ↓
Document Processing
    ↓
Chunking
    ↓
Embeddings
    ↓
Vector Database
         

                    ONLINE / QUERY TIME

User Question
    ↓
Query Processing
    ↓
Query Embedding
    ↓
Retriever
    ↓
Relevant Chunks
    ↓
Reranking / Filtering
    ↓
Context Construction
    ↓
LLM
    ↓
Grounded Answer

This distinction between indexing and query-time retrieval is important when designing production RAG systems.

Sparse Retrieval vs Dense Retrieval

Retrieval itself can work in different ways.

Two important approaches are:

Sparse retrieval

and

Dense retrieval

There is also:

Hybrid retrieval

which combines them.

Sparse Retrieval

Sparse retrieval focuses primarily on keyword or lexical matching.

A common algorithm is BM25.

Suppose the user searches:

"CVE-2026-12345 Apache vulnerability"

Exact keyword matching can be extremely useful.

Sparse retrieval is often:

• Fast

• Interpretable

• Good at exact terminology

• Useful for identifiers and technical keywords

But it may struggle when the query and document use different words for the same concept.

Research literature commonly identifies BM25 as a major sparse retrieval approach.

Dense Retrieval

Dense retrieval converts queries and documents into embeddings.

It focuses more on semantic similarity.

For example:

Query:

"How do I recover access to my account?"

Document:

"Instructions for resetting forgotten login credentials."

There may be little exact word overlap, but the meanings are closely related.

Dense retrieval can identify that semantic relationship.

Hybrid Retrieval

Hybrid retrieval combines sparse and dense approaches.

For example:

User Query
    ↓
 ┌───────────────┐
 │               │
BM25          Vector Search
 │               │
 └───────┬───────┘
         ↓
    Candidate Set
         ↓
      Reranker
         ↓
   Best Documents

This can be especially useful for technical systems where both exact terms and semantic meaning matter.

A 2025 RAG review describes sparse, dense and hybrid retrieval as major retrieval categories, with hybrid systems combining their strengths.

What Is Reranking?

Retrieval does not always return the best results in the perfect order.

Imagine the first retrieval stage returns 20 documents.

You only want the best 5.

A reranker evaluates the query and retrieved documents again and produces a better ordering.

So the pipeline becomes:

Query
 ↓
Initial Retrieval
 ↓
Top 20 Candidates
 ↓
Reranker
 ↓
Top 5 Relevant Documents
 ↓
LLM

This is often called two-stage retrieval.

The first stage emphasizes speed.

The second stage emphasizes precision.

Modern RAG systems increasingly use multi-stage retrieval and reranking to improve the quality of context supplied to the generator.

Metadata Filtering

Not every document should be searched for every user.

Suppose your knowledge base contains:

Department = HR
Department = Finance
Department = Engineering
Department = Legal

A query from an engineering employee might first filter:

department = engineering

Then perform semantic retrieval.

Other metadata might include:

• Date

• Department

• Document type

• Customer

• Product

• Region

• Access level

• Language

• Version

This can reduce irrelevant results and improve security.

Query Rewriting

Sometimes the user's original question is not ideal for retrieval.

For example:

User:

"How does it work?"

The system does not know what "it" refers to.

A query-rewriting stage can transform the question into something more useful.

Original:
"How does it work?"

↓

Rewritten:
"How does the company's employee password reset system work?"

The improved query can then be sent to the retriever.

Multi-Query Retrieval

Another technique is to generate multiple versions of the user's question.

For example:

Original:
"What are the AWS security requirements?"

Query 1:
"AWS security compliance requirements"

Query 2:
"AWS security controls"

Query 3:
"AWS cloud security policies"

Query 4:
"AWS security best practices"

Each query can retrieve different documents.

The results can then be combined.

This improves coverage when one query formulation is not enough.

HyDE

HyDE, or Hypothetical Document Embeddings, takes query transformation a step further.

Instead of directly embedding the question, an LLM generates a hypothetical answer or document.

That hypothetical text is then embedded and used for retrieval.

Conceptually:

User Question
      ↓
LLM creates hypothetical answer
      ↓
Embedding
      ↓
Vector Search
      ↓
Relevant Documents

The idea is to bridge vocabulary differences between a user's question and the documents in the knowledge base.

Contextual Retrieval

A major challenge with chunking is that a chunk can lose important context.

Imagine the original document says:

"The company introduced the new security policy in March 2026."

A chunk might contain:

"The new security policy requires MFA."

If the chunk is retrieved independently, the phrase "the new security policy" may lack context.

Contextual retrieval techniques attempt to enrich chunks with information from their surrounding document before indexing.

The goal is to make individual chunks more understandable to the retrieval system.

Parent-Child Retrieval

Another approach is to retrieve a small chunk but provide a larger parent section to the LLM.

For example:

Large Parent Document
        │
        ├── Child Chunk 1
        ├── Child Chunk 2
        ├── Child Chunk 3
        └── Child Chunk 4

The system may retrieve:

Child Chunk 2

but send:

Parent section

to the LLM.

This creates a balance:

Precise retrieval + richer context

Corrective RAG

What happens if the retrieval results are poor?

A basic RAG pipeline may simply continue.

A more advanced system can evaluate the retrieved information.

For example:

Question
   ↓
Retrieve
   ↓
Are results relevant?
   ↓
 ┌───────┴───────┐
Yes              No
 ↓                ↓
Generate       Rewrite Query
                  ↓
               Search Again

This idea is often associated with Corrective RAG, where retrieval quality influences the next step.

Self-RAG

Self-RAG introduces more adaptive behavior.

Instead of always retrieving the same way, the system can decide when retrieval is useful and can evaluate the generated response against available evidence.

Conceptually:

Question
   ↓
Should I retrieve?
   ↓
Retrieve
   ↓
Generate
   ↓
Check evidence
   ↓
Improve response

This moves RAG from a fixed pipeline toward a more adaptive architecture.

Agentic RAG

The next evolution is Agentic RAG.

Traditional RAG often follows:

Question
 ↓
Retrieve
 ↓
Answer

Agentic RAG can involve:

User Task
    ↓
AI Agent
    ↓
Plan
    ↓
Choose Tool
    ↓
Retrieve
    ↓
Analyze
    ↓
Retrieve Again
    ↓
Compare Information
    ↓
Generate Answer

An agent might decide:

• Which knowledge source to use

• Whether retrieval is necessary

• Whether another search is required

• Which tool should be called

• Whether the retrieved information is sufficient

• Whether the answer needs additional verification

Recent RAG research describes iterative retrieval-generation systems that refine queries based on previous outputs, while also noting the additional latency and risk of error propagation.

GraphRAG

Traditional RAG often represents knowledge as chunks.

But some questions are about relationships.

For example:

"Which projects are connected to the engineers who worked on Product X?"

This involves multiple relationships.

A graph can represent:

Person
  ↓
Works On
  ↓
Project
  ↓
Uses
  ↓
Technology

This can be useful for multi-hop questions.

GraphRAG combines retrieval with graph-based representations of entities and relationships.

The research literature identifies graph retrieval as particularly useful for multi-hop reasoning, although graph construction, entity linking and pruning introduce their own challenges.

Multimodal RAG

RAG is not limited to text.

A knowledge base can contain:

• Text

• Images

• Tables

• Charts

• Audio

• Video

• Documents

• Code

A multimodal RAG system can retrieve information across different modalities.

For example, a user could ask:

"What does this architecture diagram show?"

The system could retrieve:

• The diagram

• Its surrounding documentation

• Related technical descriptions

and provide those to a multimodal model.

The 2025 literature review identifies multimodal retrieval as an expanding area of RAG research.

RAG for Education

One particularly useful application is education.

Imagine an AI tutor connected to:

• Course notes

• Textbooks

• Lecture slides

• Assignments

• University policies

• Curriculum materials

A student asks:

"Explain this topic according to our course material."

The system retrieves relevant course content and generates an explanation based on that material.

A 2025 systematic survey specifically examined RAG applications in education and highlighted its potential for addressing hallucination and static knowledge limitations in educational AI systems.

Possible applications include:

• AI tutors

• Personalized learning

• Question answering

• Quiz generation

• Curriculum-based explanations

• Assignment assistance

• Educational search

Real-World Applications of RAG

RAG can be used in many industries.

Customer Support

Connect an AI assistant to:

• Product manuals

• FAQs

• Troubleshooting guides

• Support documentation

The assistant can retrieve the relevant information before responding.

Healthcare

RAG can retrieve information from approved medical or institutional knowledge sources.

However, healthcare applications require strong controls around:

• Privacy

• Data quality

• Source validation

• Access control

• Human oversight

Legal

A legal assistant can retrieve:

• Case law

• Regulations

• Contracts

• Internal legal documents

and generate summaries based on those sources.

The IJNRD paper also identifies legal case summarization as a potential RAG application.

Software Development

RAG can be connected to:

• Source code

• API documentation

• Git repositories

• Issue trackers

• Architecture documents

Developers can then ask:

"Where is authentication implemented?"

or:

"Which API handles user registration?"

The system retrieves relevant code and documentation before generating an explanation.

Enterprise Search

Instead of manually searching thousands of documents, employees can ask natural-language questions.

For example:

"What is our remote-work policy for employees in India?"

The system retrieves relevant HR documents and generates an answer.

RAG Evaluation

Building a RAG pipeline is only half the job.

You also need to measure whether it works.

Some important evaluation dimensions include:

Retrieval Quality

Did the system retrieve the correct documents?

A common metric is:

Recall@k

It measures how often relevant information appears within the top-k retrieved results.

Answer Quality

Did the generated answer correctly address the question?

Faithfulness

Is the generated response supported by the retrieved context?

Relevance

Did the retrieved information actually relate to the user's question?

Latency

How long does the complete process take?

A RAG system can be accurate but frustrating if every question takes too long.

The literature identifies retrieval quality, generation quality, factual consistency and latency as important evaluation considerations.

Common RAG Problems

RAG sounds simple:

Retrieve → Generate

But production systems are much more complicated.

1. Bad Chunking

If chunks are too small:

Context can be lost.

If chunks are too large:

Irrelevant information can enter the context.

2. Poor Retrieval

If the retriever finds the wrong documents, the LLM receives bad context.

The LLM may then produce an incorrect answer even if the model itself is capable of generating a good response.

3. Noisy Data

Duplicate or outdated documents can confuse retrieval.

This is why data preparation is extremely important.

4. Wrong Embedding Model

An embedding model that does not perform well for your domain can produce poor similarity matches.

For example, a specialized legal or medical corpus may have different requirements from a general conversational corpus.

5. Too Much Context

More context does not automatically mean a better answer.

If you retrieve 30 irrelevant chunks, the LLM has more information but potentially less useful information.

The goal is:

Relevant context, not maximum context.

6. Security Problems

RAG systems can access private information.

That means access control matters.

For example:

Employee A
   ↓
HR Documents Allowed

Employee B
   ↓
Finance Documents Allowed

The retrieval system should not simply search everything available.

7. Prompt Injection Through Retrieved Documents

Retrieved documents themselves can contain malicious or unintended instructions.

For example, a document could contain text designed to manipulate the model.

Therefore, production RAG systems should treat retrieved content as data, not automatically trusted instructions.

How to Build a Better RAG System

A practical development strategy is to improve the system layer by layer.

Start with the basics

Documents
 ↓
Chunking
 ↓
Embeddings
 ↓
Vector Database
 ↓
Top-k Retrieval
 ↓
LLM

Then measure the results.

If retrieval is poor, improve retrieval.

If answers are poor despite good retrieval, investigate generation and prompt design.

Then consider advanced techniques.

A practical improvement path

Level 1: Basic RAG

• Good document parsing

• Reasonable chunking

• Embeddings

• Vector search

• Prompt construction

Level 2: Better Retrieve

• Metadata filtering

• Hybrid search

• Query rewriting

• Reranking

Level 3: Advanced Retrieval

• Multi-query retrieval

• HyDE

• Parent-child retrieval

• Contextual retrieval

• Query decomposition

Level 4: Adaptive RAG

• Corrective RAG

• Self-RAG

• Adaptive retrieval

• Agentic RAG

Level 5: Specialized Architectures

• GraphRAG

• Multimodal RAG

• Domain-specific retrieval

• Long-document retrieval

The important lesson is that advanced RAG should solve a measured problem rather than being added simply because it is more sophisticated.

Current RAG research emphasizes trade-offs involving retrieval quality, latency, memory, cost, scalability and complexity.

RAG vs Fine-Tuning

This is one of the most common questions.

They are not necessarily competitors.

You can use both.

For example:

Fine-tuned Model
       +
RAG Knowledge Base
       ↓
Specialized AI Assistant

Fine-tuning can help the model behave correctly for a specific task, while RAG can provide current or private information.

RAG Architecture in a Production System

A production architecture might look like this:

             DATA SOURCES
                  │
       ┌──────────┼──────────┐
       ↓          ↓          ↓
      PDFs      APIs       Database
       │          │          │
       └──────────┼──────────┘
                  ↓
           Data Processing
                  ↓
              Chunking
                  ↓
             Embeddings
                  ↓
        Vector / Hybrid Store
                  │
                  │
User → Query → Retrieval
                  ↓
             Reranking
                  ↓
           Context Filtering
                  ↓
                LLM
                  ↓
        Grounded Response
                  ↓
        Citations / Sources

In larger systems, additional layers can include:

• Authentication

• Authorization

• Metadata filtering

• Query routing

• Observability

• Caching

• Evaluation

• Feedback loops

• Guardrails

• Audit logging

The Future of RAG

RAG is moving beyond the simple:

"Search some documents and put them into a prompt."

Modern research is exploring systems that can dynamically decide:

• When to retrieve

• What to retrieve

• Where to retrieve from

• How many documents to retrieve

• Whether retrieved information is sufficient

• Whether another search is required

• How to combine different sources

• How to verify the final answer

The research landscape now includes hybrid retrieval, reranking, graph-based retrieval, multimodal RAG, iterative retrieval, adaptive systems and agentic workflows.

At the same time, these systems introduce additional costs and complexity.

For example, iterative retrieval can improve multi-step reasoning but also increases latency. Hybrid systems can improve retrieval but require careful score combination and ranking. Graph systems can help with relationships but introduce challenges around entity linking and graph maintenance.

So the future of RAG is not simply about making retrieval more complicated.

It is about making retrieval more relevant, adaptive, reliable and efficient.

Final Takeaway

Retrieval-Augmented Generation provides a practical way to connect LLMs with external knowledge.

The basic idea is simple:

Retrieve relevant information → Give it to the LLM → Generate a grounded answer.

But production RAG involves much more than a vector database.

A reliable system requires attention to:

• Data quality

• Document parsing

• Chunking

• Embeddings

• Retrieval

• Hybrid search

• Reranking

• Metadata

• Context management

• Prompt design

• Evaluation

• Security

• Latency

• Cost

Basic RAG provides the foundation.

Advanced techniques such as hybrid retrieval, reranking, query rewriting, contextual retrieval, Corrective RAG, Self-RAG, GraphRAG and Agentic RAG can address specific weaknesses when they are actually needed.

The most important concept to remember is:

An LLM provides the language intelligence. RAG connects that intelligence to relevant external knowledge.

That combination is one of the key architectural patterns behind modern knowledge-intensive AI applications.

Frequently Asked Questions

1. Is RAG the same as a vector database?

No.

A vector database is one component that can be used inside a RAG system.

RAG is the overall architecture involving retrieval and generation.

2. Does RAG eliminate hallucinations?

No.

RAG can improve grounding by supplying relevant external information, but it does not guarantee that every generated statement is correct.

Retrieval quality and generation quality must both be evaluated.

3. Is RAG better than fine-tuning?

They solve different problems.

RAG is primarily useful for connecting a model to external, changing or private information.

Fine-tuning is useful for adapting model behavior, style or task performance.

They can also be combined.

4. Does RAG require a vector database?

No.

RAG can use:

• Vector search

• Keyword search

• SQL databases

• Graph databases

• Search engines

• Hybrid retrieval

• Multiple knowledge sources

Modern systems often combine multiple retrieval methods.

5. What should beginners learn first?

A good learning sequence is:

LLMs → Embeddings → Vector Databases → Basic RAG → Chunking → Retrieval → Reranking → Hybrid Search → Advanced RAG → Agentic RAG

Once you understand the basic pipeline, advanced techniques become much easier to understand.