Choose the right Retrieval-Augmented Generation architecture

Advanced RAG Pattern Selector

This interactive wizard helps enterprise AI practitioners select the most suitable Retrieval-Augmented Generation (RAG) pattern for their use case by evaluating key workload parameters. Options include GraphRAG, Self-RAG, HyDE, and standard RAG approaches.

Retrieval-Augmented Generation (RAG) integrates external knowledge with generative AI to improve accuracy and context relevance. Selecting the optimal RAG pattern depends on factors such as data structure, latency tolerance, and knowledge dynamics.

This wizard assesses your workload requirements and recommends a suitable RAG pattern—GraphRAG, Self-RAG, HyDE, or a standard RAG setup—to balance retrieval complexity, generation quality, and operational constraints.

Inputs

Data structure for knowledge base

Indicate the primary format or organization of your knowledge sources.

Acceptable response latency

Low latency (under 500 ms)Moderate latency (500 ms to 2 seconds)High latency acceptable (2 seconds+)

How fast must your AI system respond to queries?

Knowledge update frequency

How often is your knowledge base updated or changed?

Domain complexity and reasoning needs

High complexity (multi-hop, causal chains)Moderate complexity (basic inference)Low complexity (direct retrieval)

Does your use case require advanced multi-hop reasoning or simple factual lookup?

Dependence on generated text quality

Yes, output quality is criticalSomewhat importantLow importance, factual retrieval prioritized

Is it critical that the generative output is highly fluent and contextualized beyond retrieval?

Result

RAG pattern recommendation

if (dataStructure=='graph' && domainComplexity=='high') return 'GraphRAG'; else if (knowledgeUpdateFrequency=='frequent' && latencyRequirement=='low') return 'Self-RAG'; else if (generationReliance=='critical' && !(knowledgeUpdateFrequency=='frequent')) return 'HyDE'; else return 'Standard RAG';

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Recommended RAG pattern

Based on your inputs, the recommended Retrieval-Augmented Generation pattern is %s. This pattern balances your workload requirements for data structure, latency, update frequency, reasoning complexity, and generation quality.

Note

GraphRAG excels in multi-hop reasoning over structured graph data but requires more complex infrastructure. Self-RAG reduces latency by integrating retrieval and generation but demands frequent knowledge updating. HyDE improves generation quality by hallucinating contexts, suitable where output fluency is paramount. Standard RAG is simpler and widely used for unstructured documents with moderate complexity.

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