// RAG GUIDE
Types of RAG Explained Simply
Everyone says use RAG but nobody tells you there are different types. Here are the ones that actually matter — from a TikTok I posted breaking it down.
// THE TYPES
Five RAG Patterns That Matter
Traditional RAG
The foundation
The basic one. Break documents into chunks, store them in a vector database, retrieve the most relevant chunks, and feed them to the LLM. Simple. Works for most use cases. But it retrieves blindly and can pull irrelevant data.
HOW IT WORKS
- 1.Chunk documents into smaller pieces
- 2.Generate embeddings and store in a vector DB
- 3.At query time, retrieve top-k similar chunks
- 4.Feed retrieved context + query to the LLM
BEST FOR
Q&A over docs, chatbots, search — most standard use cases.
TRADEOFF
Retrieves every time regardless of need. No quality check on what it pulls.
Self-RAG
Retrieve only when needed
The model decides when it actually needs to retrieve information instead of retrieving every time. It evaluates its own output and only pulls external data when it's not confident. Saves tokens and reduces noise.
HOW IT WORKS
- 1.LLM generates an initial response
- 2.Model self-evaluates confidence in its answer
- 3.If confidence is low, triggers retrieval
- 4.Re-generates response with retrieved context
BEST FOR
Use cases where many queries can be answered from model knowledge alone.
TRADEOFF
Adds latency from self-evaluation. Model must be calibrated well to know when it doesn't know.
Corrective RAG (CRAG)
RAG that fact-checks itself
After retrieval the system checks if what it found is actually relevant. If the retrieved docs are weak or conflicting it re-queries or searches the web for better sources. RAG that fact-checks itself.
HOW IT WORKS
- 1.Retrieve documents normally
- 2.Score each document for relevance and quality
- 3.If scores are low, re-query with refined search or web fallback
- 4.Generate response only from validated sources
BEST FOR
High-stakes domains where wrong retrieval = wrong answer (legal, medical, finance).
TRADEOFF
Multiple retrieval rounds increase latency and cost. Requires a good relevance evaluator.
GraphRAG
Relationships over flat text
Instead of retrieving flat text chunks it retrieves from a knowledge graph. Entities, relationships, context. This lets the model do multi-hop reasoning like 'which teams use this tool AND report to this manager.' Vector search can't do this.
HOW IT WORKS
- 1.Build a knowledge graph from your data (entities + relationships)
- 2.At query time, traverse the graph to find connected context
- 3.Combine graph-derived relationships with vector similarity
- 4.Feed structured context to the LLM for multi-hop reasoning
BEST FOR
Complex queries requiring multi-hop reasoning, organizational data, supply chains, research.
TRADEOFF
Graph construction is expensive upfront. Maintaining the graph as data changes adds overhead.
Agentic RAG
The dominant pattern in 2026
RAG inside an agent system. Specialized agents handle query decomposition, retrieval, validation, and synthesis in parallel. The agent decides what to retrieve, when, and whether the results are good enough.
HOW IT WORKS
- 1.Agent decomposes query into sub-queries
- 2.Specialized tools handle retrieval from different sources
- 3.Agent validates and re-routes if results are insufficient
- 4.Synthesizes final answer from multiple retrieval passes
BEST FOR
Complex, multi-source tasks. Production systems that need reliability and flexibility.
TRADEOFF
Highest complexity and cost. Requires orchestration framework (LangGraph, CrewAI, etc.).
// DECISION GUIDE
Which RAG Should You Use?
Don't overcomplicate it. Start with Traditional RAG. Upgrade when you hit a wall.
Starting out or standard Q&A
Simple, proven, works for 80% of use cases.
Accuracy is critical
Self-validates retrieval quality before answering.
Need relationship reasoning
Multi-hop queries across connected entities.
Building production agents
Full control over retrieval, validation, and synthesis.
Many queries don't need retrieval
Saves tokens by only retrieving when uncertain.
// VECTOR DATABASES
Where You Store Embeddings
Every RAG system needs a place to store and search vector embeddings. These are the main options — from lightweight to enterprise-scale.
// GRAPH DATABASES
For GraphRAG & Knowledge Graphs
When you need to reason over relationships — not just similar text — you need a graph database. These power the GraphRAG pattern.
// CLOUD PROVIDER OPTIONS
Cloud-Native Vector Search
If you're already on AWS, Azure, or GCP — these services add vector search to your existing stack without a separate vector database.
Start simple. Scale when you need to.
Traditional RAG handles most use cases. Add Corrective when accuracy matters. Use Graph when you need relationships. Go Agentic when you're building agents. Don't overcomplicate it.