Top 6 RAG Architectures Every AI Engineer Should Know

📌1. Simple RAG

Definition: Simple RAG retrieves the top-k chunks from a vector store and generates an answer using a language model (LLM).

Process:

User Query → Input a query.
Retrieve (top-k) → Search for the most relevant chunks in the vector store.
Vector Store → Store the retrieved chunks.
Context (chunks) → Pass the retrieved chunks as context to the LLM.
LLM Generate → Generate the answer using the language model.
Answer → Provide the final response.

Use Cases:

FAQ bots
Internal knowledge assistants
Support systems

📌2. Hybrid RAG

Definition: Hybrid RAG combines semantic and keyword-based searches, then reranks the results for optimal relevance.

Process:

Query → Input a query.
Semantic Search → Perform a semantic search for relevant chunks.
Keyword Search → Conduct a keyword-based search.
Merge → Combine results from both searches.
Rerank → Rank the merged results based on relevance.
Top-k Results → Provide the top-ranked results.

Use Cases:

Enterprise search over messy or technical documents
Scenarios where pure vector search misses exact terms

📌3. Corrective RAG (CRAG)

Definition: Corrective RAG scores retrieved content for relevance and triggers a fallback search when the initial results are weak.

Process:

Query → Input a query.
Retrieve (top-k) → Search for the most relevant chunks.
Relevance Scoring → Evaluate the relevance of retrieved chunks.
If Good Enough, proceed to generate the answer.
If Not Good Enough, trigger a Fallback Search (different strategy/query).
Generate Answer → Provide the final response.

Use Cases:

Medical, legal, and financial domains
Scenarios where incorrect answers are costly

📌4. Self-RAG

Definition: Self-RAG allows the model to decide when to retrieve information and critiques its own output before finalizing the answer.

Process:

Query → Input a query.
Need Info? → Determine if retrieval is necessary.
If Yes, retrieve top-k chunks from the vector store.
If No, proceed to generate a draft answer.
Generate (Draft) → Create a preliminary answer.
Self-Critique (Check) → Evaluate the draft answer for accuracy.
If Good, finalize the answer.
If Not Good, refine and repeat the process.
Final Answer → Provide the validated response.

Use Cases:

Technical documentation
Deep research
Exploratory writing

📌5. Graph RAG

Definition: Graph RAG retrieves information over a knowledge graph of entities and relationships, rather than isolated chunks.

Process:

Query → Input a query.
Graph Search → Search for relevant entities and relationships in the knowledge graph.
Subgraph/Paths → Extract subgraphs or paths related to the query.
Context (entities + relations) → Pass the extracted context to the LLM.
LLM Generate → Generate the answer using the language model.
Answer → Provide the final response.

Use Cases:

Scientific discovery
Legal reasoning
Multi-hop questions where connections matter

📌6. Agentic RAG

Definition: Agentic RAG uses an agent to route queries, perform multi-step retrieval, and validate results across sources and APIs.

Process:

Query → Input a query.
Agent (Planner) → Plan and execute the retrieval process.
Tools:
Vector Store
Web Search
Knowledge Graph
APIs/Data Sources
Iterate & Refine → Refine the retrieval and synthesis process.
Synthesize + Validate → Combine and validate information from multiple sources.
Answer → Provide the final response.

Use Cases:

Automated research
Market intelligence
Executive dashboards

📌7. Flowchart Representation

QUERY
↷
RETRIEVE
↷
CONTEXT
↷
GENERATE
↷
VALIDATE
↷
ANSWER

📌8. Real-Time Story: 📚 A Day in the Life of an AI Researcher

Imagine Sarah, an AI researcher, tasked with answering a complex question about the impact of climate change on global agriculture. She uses different RAG architectures to tackle the problem:

Simple RAG: Sarah starts by querying a vector store for top-k chunks of relevant research papers. The system generates a quick summary, but she needs more depth.
Hybrid RAG: She combines semantic and keyword searches to find specific data on crop yields and reranks the results for accuracy.
Corrective RAG: When the initial results seem incomplete, the system triggers a fallback search using a broader query.
Self-RAG: Sarah critiques the generated answers, ensuring the information aligns with her research goals.
Graph RAG: She explores a knowledge graph to understand relationships between climate factors and agricultural practices.
Agentic RAG: Finally, Sarah uses an agent to synthesize data from APIs, web searches, and scientific databases, validating the findings before presenting them in her report.

Through these architectures, Sarah efficiently navigates complex data to produce accurate, actionable insights.