Retrieval-Augmented Generation (RAG) has evolved from a single blueprint into a diverse ecosystem of architectures, each designed for specific performance, scalability, and accuracy needs. Choosing the right RAG pattern is crucial for system success. This guide breaks down the major RAG architectures—how they work, when to use them, where they fail, and what alternatives to consider.
1. Naive RAG
How it works:
The simplest form of RAG. A user query is embedded, relevant chunks are retrieved from a vector DB, and passed to an LLM with a prompt template for grounded generation.
Best used when:
- Prototyping or building an MVP
- Your domain is well-defined with clean, structured docs
- Simplicity and low latency are priorities
Where it fails:
- Retrieval degradation—irrelevant context leads to hallucinations
- Poor at multi-hop or complex reasoning queries
- No mechanism to correct outdated or incorrect info
What else to use:
Try Adaptive RAG for smarter routing or Corrective RAG for self-critiquing retrieval when accuracy becomes critical.
2. HyDE (Hypothetical Document Embeddings)
How it works:
Instead of embedding the raw query, an LLM first generates a hypothetical answer. That hypothetical is embedded and used for retrieval, aiming to match the “shape” of the ideal answer.
Best used when:
- Queries are short or ambiguous
- There’s a vocabulary mismatch between queries and corpus
- Standard query embedding yields low recall
Where it fails:
- The initial generation can hallucinate, poisoning retrieval
- Adds latency with an extra LLM call
- Highly dependent on the quality of the hypothetical generation
What else to use:
Consider Hybrid RAG with lexical search for vocabulary issues, or Multimodal RAG if the query itself is multimodal.
3. Corrective RAG (CRAG)
How it works:
Adds a corrective step: retrieved docs are graded for relevance/confidence. If low, the system can trigger a web search or alternate source before generation.
Best used when:
- Factual accuracy is critical (healthcare, legal, finance)
- Your knowledge base is dynamic or partially unreliable
- You need to minimize stale knowledge hallucinations
Where it fails:
- Higher latency and complexity from grading + external search
- Web search introduces cost and unpredictability
- The grader itself can become a point of failure
What else to use:
For structured domains, Graph RAG may provide built-in verifiability. For simpler needs, a well-tuned Naive RAG with strong evaluation might suffice.
4. Graph RAG
How it works:
Uses a knowledge graph (extracted from docs) instead of or alongside a vector DB. Retrieval traverses relationships between entities, enabling multi-hop reasoning.
Best used when:
- Your domain is rich in relationships (research, fraud detection, knowledge graphs)
- Queries require multi-hop reasoning
- Explainability of retrieval paths is important
Where it fails:
- High upfront cost for graph construction/maintenance
- Can underperform on broad semantic searches vs. vector retrieval
- Not ideal for narrative or weakly-structured text
What else to use:
Hybrid RAG blending graph + vector search, or a well-chunked Naive RAG for less structured data.
5. Hybrid RAG
How it works:
Combines dense vector search and sparse (keyword) lexical search, merging results (often with Reciprocal Rank Fusion) before generation.
Best used when:
- You need both recall (lexical) and semantic understanding (vector)
- Facing vocabulary mismatch problems
- Your corpus mixes precise keywords and conceptual content
Where it fails:
- More complex to tune and balance
- Higher compute cost for dual retrieval
- Merge logic needs careful calibration
What else to use:
If keyword search is the main need, start with query expansion or BM25 before going full hybrid.
6. Adaptive RAG
How it works:
Uses an LLM-based orchestrator to classify query complexity and adapt retrieval: simple queries answered directly, complex ones trigger full RAG, multi-hop may use web search.
Best used when:
- Query complexity varies widely
- Optimizing for cost/latency is critical
- You have a clear taxonomy of query types
Where it fails:
- Routing misclassification degrades performance
- Adds system complexity
- New single point of failure
What else to use:
If query complexity is uniform, a well-optimized Naive or Hybrid RAG may be enough.
7. Multimodal RAG
How it works:
Extends retrieval to multiple modalities (text, images, audio). A multimodal query retrieves multimodal chunks, and a multimodal LLM generates the answer.
Best used when:
- Your knowledge base and queries are inherently multimodal (manuals with diagrams, medical imaging, product catalogs)
- Answers require cross-modal synthesis
Where it fails:
- High complexity in alignment, chunking, and fusion
- Cost and latency are significantly higher
- Early-stage tooling
What else to use:
For mostly text-based tasks, use text RAG with separate image captioning or object detection pipelines.
8. Agentic RAG
How it works:
Embeds RAG within an agent framework. Agents with planning (ReAct) and memory use RAG as a tool for multi-step research across sources (local, cloud, web via MCP servers).
Best used when:
- Tasks need autonomous, multi-step research (due diligence, competitive analysis)
- Problem scope is broad and not limited to one knowledge base
- Long-term memory across sessions is required
Where it fails:
- Highest complexity and unpredictability
- Prone to goal drift or infinite loops
- Very high operational cost
What else to use:
For deterministic knowledge lookup, a simpler RAG is more reliable and cost-effective. Agentic RAG is for open-ended exploration.
Conclusion: Start Simple, Scale Thoughtfully
There’s no one-size-fits-all RAG. The best choice depends on your specific requirements for accuracy, latency, cost, and complexity.
- Start with Naive RAG and invest in data prep and evaluation.
- Identify your bottleneck: retrieval quality → HyDE/Hybrid; reasoning → Graph; factuality → Corrective.
- Move to Adaptive/Agentic only when clear production needs emerge.
The simplest RAG that meets your accuracy, latency, and cost constraints is usually the right one.
Further reading:
- Lewis et al., Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks
- Gao et al., Precise Zero-Shot Dense Retrieval without Relevance Labels
- Sarthi et al., Corrective Retrieval Augmented Generation
- Wu et al., Knowledge Graph-Augmented Language Models for Knowledge-Grounded Dialogue