Types of Retrieval-Augmented Generation: 10 RAG Architectures and When to Use Each

Choosing the right architecture is super important. It saves you from rebuilding the system later. Therefore, understand your retrieval problem and choose an architecture accordingly. You can change or upgrade your RAG anytime.

This guide covers 10 types of retrieval augmented generation, how they work, their limitations, and how to choose the right RAG architecture for your use case.

Why Retrieval-Augmented Generation Isn't One Architecture

Retrieval-Augmented Generation is not one architecture because one retrieval method can not handle all types of data and query. All the RAG systems prepare data, retrieve information, rank results, and give context to the language model differently.

The simple RAG system retrieves information once and generates an answer. However, the advanced architectures improve retrieval, rerank results, search multiple sources, and perform multiple retrieval steps. In short, RAG has different architecture to solve different retrieval problems.

10 Types of Retrieval-Augmented Generation at a Glance

Here is a quick comparison of the 10 common RAG architectures.

1. Naive (Simple) RAG

Naive RAG is the simplest retrieval-augmented generation architecture. It does not support advanced search or additional processes. Therefore, it can only handle basic retrieval tasks.

How It Works

First, the Native RAG breaks the documents into small sections, converts the sections into embeddings, and saves them in a vector database.

The RAG converts the user query into an embedding and searches the database for similar sections. It sends the most relevant sections to the LLM as context to give an answer.

The basic flow is:

Query → embedding → vector search → top-k chunks → LLM prompt → answer

When to Use It

Naive RAG works best for text-based data(from one or two sections) and simple questions.

For example, you can use it for:

• HR chatbot for company policies.
• Internal assistant for product information
• Customer support bot for common questions

Limitations

• Similarity search can retrieve relevant text with the wrong answer
• Misses exact terms like SKUs, error codes, names, and legal references
• Poor sectioning can separate important information
• Struggles with questions that require information from multiple documents

2. Hybrid RAG (Keyword + Vector Retrieval)

Vector search is ideal for finding information with similar meaning. However, some queries need an exact match. Hybrid RAG combines vector search with keyword search to handle both types of queries. It is a useful upgrade to basic RAG because it improves retrieval without any complexity.

How It Works

The Hybrid RAG performs two searches for the same query. Vector search finds content with a similar meaning and the keyword algorithm like BM25 looks for exact words and phrases. Reciprocal Rank Fusion (RRF) then combines the results from both searches.

For example, imagine a technician searches: “How do I fix error E1027?”

Vector search finds documents about similar system failures and the keyword search finds documents containing the exact E1027 error code. Hybrid retrieval combines both results to rank the most relevant document high.

When to Use It

Hybrid RAG is useful for:

• Product catalogs with SKUs and model numbers
• Technical documents with specific error codes
• Legal documents with case numbers and legal references

Limitations

• Requires tuning to balance keyword and vector search
• Duplicate or similar results require reranking
• Struggles with questions involving connected information

3. Advanced RAG (Pre- and Post-Retrieval Optimization)

Advanced RAG improves the retrieval process before and after the search. It allows the model to receive more relevant and accurate context for generating an answer.

How It Works

Advanced RAG improves the search query, applies metadata filters, and divides documents into useful sections. It also reranks search results to place the most relevant information at the top.

Common techniques include:

• Query rewriting
• Semantic or hierarchical chunking
• Metadata filtering
• Cross-encoder reranking

Reranking helps Advanced RAG select the most relevant information from the initial search results. It compares each result with the query and brings the best matches at the top.

When to Use It

Advanced RAG is useful when:

• The right documents rank too low
• Large documents split into poor sections
• Users ask unclear questions
• Your data includes useful metadata like product, date, or department

Check retrieval before changing the LLM. In most cases, the model gives a wrong answer because it does not receive the right information.

Limitations

• Additional retrieval steps increase complexity
• Reranking increases latency and cost.
• Poor evaluation makes it difficult to identify the changes

4. Graph RAG

Graph RAG connects people, companies, products, locations, and events through their relationships. It uses these connections to answer questions.

How It Works

Graph RAG organizes information into a knowledge graph with entities and their relationships. When a user asks a question, it follows these relationships to find relevant information and sends it to the LLM to create an answer.

For example:

Supplier A → owned by → Company B → operates in → Country C

When to Use It

Graph RAG is best for questions that require connected information, like

• Which suppliers are affected by a regulation through their subsidiaries?
• How are customers, accounts, and transactions connected?
• Which research papers share authors or topics?

These questions require information from multiple connected records.

Limitations

• Building and maintaining a knowledge graph requires more engineering
• Graph Indexing increases the cost Graph RAG increases system complexity and requires more engineering

5. Multimodal RAG

Multimodal RAG retrieves and uses information from text, images, audio, and video. It is useful when your data includes diagrams, product images, scanned documents, or recordings.

How It Works

Multimodal RAG retrieves information from text, images, audio, and video. It converts all types of content into embeddings and searches them for relevant information. The multimodal model uses the retrieved content to understand the query and generate an answer.

When to Use It

Common production scenarios include:

• Searching technical manuals with diagrams
• Reviewing insurance claims with forms and photos
• Finding information from medical images and reports
• Finding products using customer-uploaded photos

Limitations

• Multimodal indexing is more complex than text indexing
• Images, audio, and video require more storage and computing power
• Multimodal models and embeddings increase query costs

6. Agentic RAG

Agentic RAG uses AI agents to manage the retrieval process. The agent decides what information to retrieve, which sources or tools to use, and when to perform additional searches.

How It Works

Instead of retrieving information once, the agent decides when to retrieve information, where to search, and whether it needs another search.

For example, an agent can follow this process:

Understand the question → search internal documents → query an API → compare results → retrieve more information → generate an answer

The agent can also rewrite queries or use different tools when needed. This makes Agentic RAG highly useful for complex questions.

When to Use It

Agentic RAG works well for:

• Research that requires multiple sources
• Financial analysis using documents, databases, and APIs
• Support systems that retrieve information and perform actions
• Complex questions that require multiple searches

Limitations

• One query can take different retrieval paths
• Multiple tool calls make errors harder to track
• More retrieval steps increase response time
• Multiple model calls increase token usage and cost

Therefore, Agentic RAG should not be the default choice for simple document Q&A. Use it when the task genuinely requires multiple decisions or retrieval steps.

7. Self-RAG (Self-Reflective Retrieval)

Self-RAG checks if the retrieved information is relevant and evaluates the answer before producing the final response.

How It Works

Self-RAG allows the model to decide when it needs retrieval and evaluate the retrieved information and response. The original Self-RAG framework uses special reflection tokens to guide these decisions.

A simple process looks like this:

Retrieve → evaluate context → generate answer → evaluate answer → retrieve again or revise if needed

If the information does not support the answer, the model retrieves more information or revises its response.

When to Use It

Self-reflective approaches are useful for

• Legal assistants checking answers against relevant cases
• Compliance systems answering questions about policies
• Medical systems checking whether retrieved information supports the answer

Limitations

• Self-RAG requires additional training or prompting
• Each evaluation step increases latency and cost
• More model decisions increase system complexity
• Simple retrieval tasks usually do not need Self-RAG

8. Corrective RAG (CRAG)

Corrective RAG (CRAG) checks the quality of retrieved information before generating an answer. If the retrieved information is not relevant, CRAG corrects the retrieval process and searches for another source.

How It Works

A retrieval evaluator checks if the retrieved documents can answer the question. If the results are poor, CRAG searches another source like the web, filters out irrelevant content to give an accurate answer.

The process looks like this:

Retrieve → evaluate → use results or find better information → generate

When to Use It

Corrective RAG works well for:

• Customer support assistants.
• Knowledge bases with missing information.
• Systems that use multiple information sources.
• Applications that require highly accurate answers.

Limitations

• Evaluators can make wrong decisions.
• Fallback searches increase response time
• External sources add security and reliability risks
• Missing or unreliable information requires clear handling rules

9. Adaptive RAG

Adaptive RAG chooses a retrieval strategy that matches the user’s query. This RAG skips retrieval for simple queries, uses a single search for factual questions, and performs multiple searches for complex questions.

How It Works

Adaptive RAG first checks how complex the query is to select a suitable retrieval method:

Simple request → no retrieval

Factual question → single retrieval

Complex question → multiple retrieval steps

This allows the RAG to use more retrieval when needed

When to Use It

Adaptive RAG works well when:

• Queries have different level of processing
• Some queries do not need retrieval
• Only complex queries need multiple retrieval steps

Limitations

• Incorrect routing selects the wrong retrieval approach
• The router needs regular testing and monitoring
• Query complexity is difficult to identify.
• More retrieval paths make testing harder

10. Modular RAG

Modular RAG is the end-state of a mature RAG setup. It divides the RAG into separate components. Teams can change or replace these modules independently. Additionally, they can also add a new retrieval source without rebuilding the existing system.

How It Works

Modular RAG separates the main parts of the RAG process into modules:

Data source → indexing → retrieval → filtering → reranking → generation → evaluation

When to Use It

Modular RAG is useful when:

• Each team handles a part of the system
• Models, databases, or retrieval methods change often
• The system uses multiple data sources
• An existing RAG system becomes difficult to maintain

Limitations

• Building separate modules requires more engineering
• Errors are hard to track
• More configurations require more testing
• Small RAG setup usually do not need this complexity

How to Choose the Right RAG Architecture

Choosing the right RAG architecture can be tricky because each architecture solves a different retrieval problem. The advanced RAG may not always help, you have to identify the problem and choose an architecture that addresses it.

Here is how you can make the decision.

1. Diagnose the Failure Before Choosing the Architecture

First, identify the cause of the problem in your RAG setup. If the correct information exists in your knowledge base but the system cannot find it, the problem is more likely retrieval. In this case, changing the LLM will not solve the problem.

Here are some common symptoms and the first solutions worth testing.

2. Four Questions to Answer Before You Build

There are four questions you have to ask to find the right RAG architecture.

What does your data look like?

First, you have to find out what your data is. If it’s text, images, diagrams, or connected data that may need different retrieval methods.

How often does the data change?

Check how often your data changes? If your data updates frequently, use faster indexing to keep the information current. Otherwise, RAG can retrieve outdated information.

What can you spend on each query?

Consider how much you can spend on each query. Reranking, model calls, agent steps, and reflection loops increase the processing cost. The cost increases further as query volume grows.

What happens if the answer is wrong?

Check the consequences of an incorrect answer. High-risk systems need stronger evaluation and more reliable information sources to reduce errors.

3. Start With the Simplest RAG You Can Measure

Start with a simple RAG architecture and measure its performance. Check whether it retrieves the correct information, ranks the right sections at the top, generates answers from the retrieved information, and meets your cost and latency requirements. Reviewing this information is important to know where the system needs improvement.

Add complexity only when you identify a specific problem. Hybrid retrieval is best for missed exact terms, reranking for poorly ranked results, Graph RAG works for relationship-based questions, and Adaptive RAG controls unnecessary processing. Build a simple and most cost-effective RAG architecture that meets your accuracy, latency, and reliability requirements.

How RAG Systems Are Built for Production

A RAG prototype is surprisingly simple. You have to load some documents, create embeddings, connect a vector database to a Large Language Model(LLM) and ask questions. However, the production is different.

A reliable RAG setup depends on several engineering layers. A problem in any RAG layer impacts the final answer.

Data ingestion and preparation

The system first collects data from documents, databases, APIs, websites, and internal business systems. It cleans and processes the data before indexing it. Poor-quality, duplicate, or outdated data reduces retrieval accuracy.

Chunking and indexing

Divide large documents into small sections, index these sections so the system can find relevant information during retrieval. Good sectioning helps the system find the right information and improves retrieval accuracy.

Retrieval

The retrieval layer finds relevant information. It uses vector search, keyword search, metadata filters, graph retrieval, and all these methods to find the best information.

Reranking

A reranker reviews the initial search results and ranks the most relevant ones high. This helps the LLM receive better context and more relevant information.

Generation

RAG sends the selected context to the Large Language Model with instructions to generate the answer. Prompt design, context size, citation requirements, and model choice influence the final response.

Evaluation

Evaluate retrieval and generation separately to measure RAG performance. This allows you to find out whether the problem comes from retrieval or the model’s response.

Observability

Monitor logs and performance metrics to monitor the RAG. Record retrieved information, sources, response time, errors, etc. This makes it easier for development teams to identify problems and improve system performance.

Security

Use access controls to protect sensitive business information. Only retrieve documents the user has permission to access, even from a shared database.

Ongoing maintenance

Models, data, and user queries change frequently. Therefore, RAG requires continuous maintenance to keep retrieval accurate and reliable. Evaluate, reindex, monitor, and improve the RAG setup regularly.

Need Help Choosing and Building the Right RAG Architecture?

You have to take into account your data, retrieval needs, accuracy goals, latency requirements, security, and budget to find the right RAG Architecture. Adding advanced components makes the system complex and costly.

Coding Crafts helps businesses design and build production-ready RAG setup. Our team works with vector retrieval, hybrid search, reranking, knowledge graphs, multimodal retrieval, adaptive routing, and agentic workflows. We try our best to build RAG systems that are accurate, secure, and easier to maintain.