📄 Architecture Guide — 22 min read
Top 6 RAG Architectures
Every AI Engineer Must Know
RAG is not a single pattern. There are 6 architectures — each solving a different retrieval problem. Pick the wrong one and your chatbot confidently hallucinates. Pick the right one and it becomes genuinely useful in production.
⚡ At a Glance — The 6 Architectures
1
Simple RAG — query → vector search → generate. Fast to build, first thing to try.
2
Hybrid RAG — dense + sparse search merged with re-ranking. Production standard.
3
Corrective RAG — scores retrieved content, triggers fallback when quality is low.
4
Self-RAG — model decides when to retrieve and critiques its own output.
5
Graph RAG — retrieves over entity relationships, not isolated chunks.
6
Agentic RAG — agent plans, routes, and validates across multiple sources.
✓
Start with Hybrid RAG (2). It solves 80% of production problems. Layer in others as you hit specific limits.
Why Simple RAG Breaks in Production
Simple RAG works in demos. It fails in production because:
❌ Exact-term queries
Dense-only search misses “AWS EC2 t3.micro pricing” because the embedding doesn’t capture exact product codes.
❌ Irrelevant retrieved chunks
Top-K retrieval returns tangentially related content. LLM generates plausible-sounding but wrong answers.
❌ Multi-hop questions
“What did the CEO say about the product launch mentioned in the Q3 report?” requires 2+ retrieval steps.
Each architecture below solves one or more of these failure modes. The right one depends on your specific problem.
1. Simple RAG — The Baseline
The foundation. Every other architecture builds on this. Understand it cold before moving to anything more complex.
Simple RAG Pipeline
❓
User Query→
📋
Embed QuerySame model as ingestion→
🗄
Vector StoreTop-K similarity search→
📄
Context ChunksInject into prompt→
🧠
LLM GenerateAnswer→
✓
AnswerBest for: FAQ bots, internal knowledge Q&A, support chatbots with clean structured docs
Fails at: Exact-term queries, messy/overlapping docs, multi-hop questions, noisy corpora
Stack: LlamaIndex or LangChain + any vector DB + OpenAI/Anthropic embeddings
2. Hybrid RAG — The Production Standard
Hybrid RAG combines dense retrieval (semantic similarity) with sparse retrieval (BM25 keyword matching), then merges results with Reciprocal Rank Fusion (RRF) and re-ranks with a cross-encoder. This is what most mature production systems use.
Hybrid RAG Pipeline
❓
Query↗
↘
📋
Dense SearchSemantic embedding similarity🔍
BM25 SparseKeyword & exact-term matching→
+
RRF MergeReciprocal Rank Fusion→
📏
Re-rankCross-encoder scores🧠
GenerateTop-K to LLMBest for: Enterprise search, technical docs with product codes, legal/medical where exact terms matter
Why it wins: Dense catches semantic intent. BM25 catches “GPT-4o” or “Section 4.2.1”. RRF merges them fairly.
Stack: Weaviate or Qdrant (hybrid built-in) + Cohere Rerank or BGE-reranker
Production tip
Start here. Hybrid RAG with a cross-encoder re-ranker outperforms naive RAG on almost every real-world benchmark. The added latency (50–150ms for re-ranking) is almost always worth the accuracy gain. Only move to more complex patterns if Hybrid RAG still fails on your specific use case.
3. Corrective RAG (CRAG) — Knows When to Give Up
CRAG adds a relevance evaluator between retrieval and generation. If the retrieved content scores below a threshold, it triggers a fallback search with a different strategy or query — rather than generating from bad context.
Corrective RAG (CRAG) Pipeline
❓
Query→
🗄
Retrieve Top-K→
📊
Relevance ScoreLLM or classifierGood?
→ YES
🧠
Generate→ NO (weak context):
Rewrite query → Web search / different retrieval strategy → Merge new results → Generate with better context
✓ Answer
Best for: Medical, legal, financial applications where a wrong answer has real consequences
Key insight: It is better to say “searching further” than to answer from weak context. CRAG catches bad retrieval before it reaches the LLM.
Stack: LangGraph (built-in CRAG node) + Tavily for web fallback + any LLM for relevance scoring
4. Self-RAG — Thinks Before It Retrieves
Self-RAG teaches the model to decide for itself whether retrieval is even needed for a given query — and to critique its own output before returning it. It produces a draft, checks if it is grounded, and revises if not.
Self-RAG Pipeline
❓
Query→
🧠 Need to retrieve?LLM generates a “retrieve” or “skip” token. If the answer is already known (e.g. general knowledge), skip retrieval entirely.
YES → RetrieveVector search top-K
NO → Generate directlySkip retrieval step
📄 Retrieved ContextScored for relevance before use. Irrelevant chunks filtered out.
→
🧠 Generate DraftLLM produces initial response with context
→
🔎 Self-CritiqueLLM checks: Is the answer grounded in context? If NO → revise draft.
✓ Verified Answer
Best for: Technical documentation search, deep research tools, exploratory writing assistants
Why it’s different: The model has agency over retrieval. It avoids unnecessary retrieval calls, saving cost and latency for simple queries.
Stack: Requires custom training or fine-tuning with Self-RAG tokens. LangGraph can simulate it with a reflection loop.
5. Graph RAG — Relationships Matter
Graph RAG retrieves over a knowledge graph of entities and relationships — not isolated text chunks. Instead of “find similar paragraphs”, it asks “find connected entities and traverse their relationships.”
Graph RAG Pipeline
❓
Query→
📈 Graph SearchEntity recognition → Find nodes in knowledge graph → Traverse edges & relationships → Subgraph / paths
→
📋 Context AssemblyEntities + relationships + associated text chunks assembled into rich context
→
🧠
Generate
Example query: “What drugs interact with the medication prescribed for the condition mentioned in the patient’s last appointment?”
Traditional RAG returns chunks about drugs. Graph RAG traverses: Patient → Appointment → Condition → Medication → Drug Interactions → Answer.
Traditional RAG returns chunks about drugs. Graph RAG traverses: Patient → Appointment → Condition → Medication → Drug Interactions → Answer.
Best for: Scientific discovery, legal reasoning, healthcare, any domain where entity connections matter more than text similarity
Trade-off: Building and maintaining the knowledge graph is significant upfront work. Not worth it unless multi-hop reasoning is a core requirement.
Stack: Neo4j or Amazon Neptune + LlamaIndex Graph RAG module + Microsoft GraphRAG (open-source)
6. Agentic RAG — Retrieval with a Brain
Agentic RAG gives an AI agent control over the entire retrieval process. The agent plans which sources to query, routes to the right tool, runs multi-step retrieval, and validates results before generating — like a human researcher, not a search engine.
Agentic RAG Pipeline
❓
Query→
🤖 Agent (Planner)
Analyses query intent → Decides retrieval strategy → Routes to appropriate tools → Evaluates results → Re-queries if needed → Synthesises final answer
→
🗄 Vector StorePrivate docs, company knowledge
🌐 Web SearchLive data via Tavily/SerpAPI
📈 Knowledge GraphEntity relationships
📊 APIs / Data SourcesDatabases, CRM, live feeds
↓ Synthesise + Validate across all sources ↓
✓ Grounded, Multi-Source Answer
Best for: Automated research, competitive intelligence, executive dashboards, complex enterprise Q&A over multiple systems
Trade-off: High token cost, higher latency (3–10s), harder to debug. Only use when simpler RAG genuinely cannot solve the problem.
Stack: LangGraph or CrewAI + LlamaIndex for retrieval nodes + LangSmith for observability
Which Architecture Should You Use?
| Your situation | Use this | Why |
|---|---|---|
| First RAG app, clean docs, simple Q&A | Simple RAG (1) | Ship fast, validate the concept |
| Production app, mixed doc quality, technical terms | Hybrid RAG (2) | Beats naive RAG on almost every benchmark |
| Domain where wrong answers are expensive | CRAG (3) | Fallback prevents bad-context hallucination |
| Expensive retrieval, want to skip when unnecessary | Self-RAG (4) | Model decides when to retrieve, saves cost |
| Multi-hop questions over entities & relationships | Graph RAG (5) | Only architecture designed for relational reasoning |
| Multiple data sources, agent needs to choose strategy | Agentic RAG (6) | Agent plans and executes the right retrieval path |
| Not sure which you need | Start with Hybrid (2) | Fixes most failure modes, easiest to reason about |
“The best RAG system is the simplest one that works for your data. Start with Hybrid RAG. Move to more complex patterns only when you have evidence the simpler approach is failing.”
❌ Most common mistake: jumping to Agentic RAG because it sounds impressive
Agentic RAG has 5× the complexity, 10× the token cost, and is much harder to debug. Most problems that teams solve with Agentic RAG could have been solved with Hybrid RAG + a cross-encoder re-ranker. Complexity should be earned by specific failure modes, not assumed upfront.
Fix: Implement Hybrid RAG first. Build a 50-question golden test set. Measure failure modes specifically. Only then choose the architecture that addresses YOUR failures.
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