Production RAG Architecture Patterns

Retrieval-Augmented Generation has evolved from a simple retrieve-then-generate pattern into a sophisticated family of architectures, each optimized for different use cases and quality requirements. Understanding these patterns and their tradeoffs is essential for building production-grade AI systems that deliver accurate, grounded responses.

This research examines eight distinct RAG architecture types, evaluation frameworks, chunking strategies, and vector database selection criteria based on peer-reviewed research and production deployments.

Eight RAG Architecture Types

1. Simple RAG

The foundational retrieve-then-generate pipeline remains effective for many use cases:

1. Query → Embedding
2. Vector Search → Top-K Retrieval
3. Context Assembly → LLM Generation

Typical latency: 1-3 seconds. Simple RAG works well for FAQ systems and document Q&A where queries map directly to retrievable content.

2. Memory RAG

Memory RAG integrates conversation history for coherent multi-turn dialogue. Using patterns like ConversationBufferMemory, the system maintains context across interactions while still retrieving relevant documents for each query.

This architecture excels in customer support scenarios where understanding conversation context is essential for appropriate responses.

3. Branching RAG

Branching RAG implements multiple retrieval paths with parallel execution. Queries route to specialized retrievers (Q&A, summarization, factual lookup) based on intent classification.

Performance benchmarks show 15-30% better recall than single-path approaches, though at the cost of increased infrastructure complexity.

4. Adaptive RAG

Adaptive RAG dynamically adjusts retrieval parameters based on query complexity. The system modifies k (number of retrieved documents), retrieval method (dense vs. sparse vs. hybrid), and re-ranking aggressiveness based on query characteristics.

This pattern proves particularly effective for heterogeneous corpora where different document types benefit from different retrieval strategies.

5. Corrective RAG (CRAG)

Yan et al. introduced CRAG in January 2024 (arXiv:2401.15884), implementing a self-correction mechanism with a lightweight retrieval evaluator that returns confidence scores.

Three-Tiered Action System

• Correct (high confidence): Proceed with retrieved documents
• Incorrect (low confidence): Trigger web search fallback
• Ambiguous (medium confidence): Combine internal and external sources

CRAG uses a decompose-then-recompose algorithm, breaking complex queries into sub-questions and validating each retrieval independently.

6. Self-RAG

Asai et al. introduced Self-RAG in October 2023 (arXiv:2310.11511), implementing self-reflection through special tokens that guide the generation process.

Reflection Tokens

• Retrieve: Should I retrieve for this segment?
• IsRel: Is retrieved content relevant?
• IsSup: Is generation supported by context?
• IsUse: Is generation useful for the query?

Performance Results

Metric	Self-RAG	Baseline
PopQA Accuracy	55.8%	14.7% (Llama2-13B)
Fact Verification	81%	71% (other techniques)
Biography Factuality	80%	71% (ChatGPT)

7. Graph RAG

Microsoft introduced Graph RAG in April 2024 (arXiv:2404.16130), combining knowledge graph construction with community summarization using the Leiden algorithm.

The approach constructs entity-relationship graphs from source documents, then uses community detection to create hierarchical summaries that can answer global questions about the corpus.

8. Agentic RAG

Agentic RAG represents the most sophisticated pattern, implementing agent-based retrieval with tool use, autonomous decision-making, and multi-hop reasoning across knowledge bases.

The agent decides when to retrieve, which sources to query, how to combine information, and when sufficient information has been gathered. This pattern enables complex research tasks that require synthesizing information from multiple sources.

RAGAS Evaluation Framework

RAGAS (arXiv:2309.15217) provides reference-free evaluation using LLM-based metrics, enabling automated quality assessment without ground truth labels.

Core Metrics

Metric	Measures	Method
Faithfulness	Factual consistency with context	Claim extraction and verification
Answer Relevancy	Pertinence to query	Reverse-engineered question comparison
Context Precision	Signal-to-noise ratio	Relevant chunk ranking analysis
Context Recall	Retrieval completeness	Ground truth comparison (when available)

Score Interpretation

• 0.8-1.0: Excellent — production ready
• 0.6-0.8: Good — may need optimization
• Below 0.4: Poor — requires substantial changes

RAGAS integrates with Langfuse, Datadog, and Evidently AI for production monitoring.

Chunking Strategies

Chunking strategy significantly impacts retrieval quality. Benchmarks reveal substantial performance differences:

Strategy	Accuracy	Recall	Cost
Page-level	0.648	High	Low
Semantic	+9% vs baseline	High	High
RecursiveCharacter (512)	Baseline	85-90%	Low
LLM-based	Highest	Highest	Very High

Best Practices

• General use: RecursiveCharacter with 400-512 tokens and 10-20% overlap
• High-value documents: Semantic chunking with embedding-based boundary detection
• Structured documents: Respect document structure (headers, sections) in chunking

Vector Database Comparison

Database	Latency (p50)	Max Scale	Hybrid Search	Open Source
Pinecone	<10ms	Billions	Yes (API)	No
Weaviate	<50ms	Billions	Native	Yes
Qdrant	<10ms	Billions	Native	Yes
Chroma	~20ms	Millions	Approximate	Yes

Selection Framework

• Need managed + minimal ops → Pinecone
• Need hybrid search + flexibility → Weaviate or Qdrant
• Prototyping/lightweight → Chroma

Hybrid Search Best Practices

Hybrid search combines sparse (BM25) and dense (embedding) retrieval for improved recall. The recommended pipeline:

1. Parallel Retrieval: BM25 (top-K) + Dense (top-K) simultaneously
2. Fusion: Reciprocal Rank Fusion (RRF) with k=60
3. Re-ranking: Cross-encoder or ColBERT (optional but recommended)
4. Final Selection: Top-N for generation

Performance: 15-30% better recall than either method alone, with particularly strong gains on queries mixing keyword-oriented and semantic components.

Advanced Techniques (2024-2025)

HyDE (Hypothetical Document Embeddings)

HyDE generates a hypothetical answer document first, then uses its embedding for retrieval. This approach significantly enhances retrieval precision for complex queries by bridging the query-document vocabulary gap.

query → LLM generates hypothetical answer →
embed hypothetical → retrieve similar documents

Query Decomposition

Techniques including RQ-RAG, GMR, and RAG-Fusion decompose complex queries into simpler sub-queries. Results show 35% gain in document-level precision (arXiv:2510.18633) for multi-faceted questions.

Implementation Recommendations

1. Start with Simple RAG to establish baselines before adding complexity
2. Implement RAGAS evaluation early to measure improvement quantitatively
3. Use hybrid search as a default for production systems
4. Consider CRAG or Self-RAG when faithfulness metrics need improvement
5. Deploy Graph RAG only for large corpora requiring holistic understanding
6. Reserve Agentic RAG for complex multi-source research tasks

References

• Asai et al. (2023). "Self-RAG: Learning to Retrieve, Generate, and Critique." arXiv:2310.11511
• Yan et al. (2024). "Corrective RAG." arXiv:2401.15884
• Edge et al. (2024). "From Local to Global: A Graph RAG Approach." arXiv:2404.16130
• Es et al. (2023). "RAGAS: Automated Evaluation of Retrieval Augmented Generation." arXiv:2309.15217
• Gao et al. (2024). "Retrieval-Augmented Generation for Large Language Models: A Survey." arXiv:2312.10997