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Interactive Architecture Tool

Which AI Retrieval Architecture Is Right for You?

Bare LLM, VectorRAG, GraphRAG or KnowledgeGraphRAG? Set your context and see how four architectures score across six quality dimensions. Grounded in published research from Gartner, NVIDIA, Lettria, and peer-reviewed studies.

Your Context

Select the options that best describe your requirements. Scores update instantly.

Query complexity

Cross-document reasoning

Data structure

Documents, articles, transcripts

Hallucination tolerance

Errors carry business cost

Query nature

Open-ended natural language

Data currency

Fixed knowledge base

Build investment

Weeks with engineering support

Language scope

Monolingual

Provenance tracking

No audit trail needed for AI outputs

Error cost

Customer-facing, operational decisions

Query volume

1,000 – 50,000 queries per month

Composite Score

TOP PICKLevel 3KnowledgeGraphRAG
78
Level 2GraphRAG
75
Level 2.5Agentic RAG
64
Level 1.5Corrective RAG
48
Level 1VectorRAG
38
Level 0Bare LLM
22

Recommendation

KnowledgeGraphRAGFull neuro-symbolic

KnowledgeGraphRAG scores highest across your parameters. Every inference traces to a typed, authoritative source. The investment in ontology design pays dividends in accuracy, explainability, and audit readiness.

Discovery Question

“What is the cost, in time, money, or trust, of a wrong answer from your AI system today?”

Narrow margin: 3 pts vs GraphRAG. A hybrid approach is worth evaluating.

Quality Dimension Breakdown

Dimension
Bare
LLM
VECTORRAG
Corrective
RAG
GRAPHRAG
Agentic
RAG
Knowledge
GraphRAG
Factual Accuracy
30
62
73
92
76
83
Hallucination Safety
30
60
72
70
68
85
Multi-hop Reasoning
5
11
25
88
75
84
Comprehensiveness
52
53
63
74
72
57
Explainability
15
34
38
58
42
74
Compliance & Audit
8
20
25
55
35
78

Architecture Reference

Bare LLMLevel 0
Pure neural
22

The model answers from what it learned during training. Nothing connects its output to your data. Works for exploration and drafting. Breaks wherever accuracy or auditability matters.

⚠

Hallucinated answers stated with full confidence. No reasoning path to audit.

VectorRAGLevel 1
Neural + similarity search
38

Retrieval-Augmented Generation splits your documents into chunks and retrieves by vector similarity. Reduces hallucinations sharply on direct lookups. Fails on relational or structured queries. It finds similar text, not connected facts. Gartner D&A 2026: 44% of organizations implemented a semantic layer in 2025. Only 14% are confident their data is governed.

⚠

Zero accuracy on schema-constrained, KPI-class, or multi-hop queries (Diffbot: 0% on strategic planning).

Corrective RAGLevel 1.5
Neural + evaluated retrieval
48

Adds a retrieval evaluator that scores retrieved documents as Correct, Incorrect, or Ambiguous, triggering different actions for each. When retrieval is Incorrect, falls back to web search; when Ambiguous, applies decompose-then-recompose to extract relevant portions. Peer-reviewed at ICLR 2024. Achieves 74.8% on PubHealth vs 39.0% for Standard RAG — a 35.8-point improvement through retrieval correction alone.

⚠

Web search fallback does not work with proprietary or air-gapped data. Adds per-query evaluation cost. Still vector-based retrieval — cannot follow entity relationships.

GraphRAGLevel 2
Proto neuro-symbolic
75

An LLM extracts entities and relationships from documents at indexing time, building a graph. Enables multi-hop reasoning and global sensemaking. The graph carries noise from the extraction process, but it handles a class of questions vector-only systems cannot reach. Prior art: Prasad Yalamanchi published this pipeline in 2020: 'Text to Knowledge Graph' (The Startup, Medium), four years before Microsoft Research named it GraphRAG.

⚠

LLM extraction errors compound in the graph. Non-English languages introduce significant noise. High indexing cost.

Agentic RAGLevel 2.5
Agent-supervised retrieval
64

Deploys a reasoning agent (modelled as a Markov Decision Process) that plans retrieval, verifies claims, and iterates until confident. RAG-Gym benchmarks show 25.6% improvement over baselines. The agent constructs an initial answer, identifies unsupported claims, then searches for verification. Performance plateaus at 500–1,000 training samples — no further improvement through additional training.

⚠

High per-query reasoning cost that compounds at scale. Requires RL training and process reward models. Every query pays the full reasoning cost — no amortisation.

KnowledgeGraphRAGLevel 3
Full neuro-symbolic
78

A pre-built, expert-maintained ontology with typed entities and formal relationships. The LLM handles natural language; the Knowledge Graph handles logic and structured facts. Every inference is traceable. KnowledgeGraphRAG is agglomerative: it subsumes all lower-level techniques as building blocks. Gartner (2026) named Decision Governance the most underrated trend in enterprise AI. A curated Knowledge Graph is the only retrieval architecture that answers that question natively.

⚠

Ontology design, entity modelling, and domain expert review cannot be shortcut. Requires ongoing domain expertise to maintain. Not suited to fast-moving live data without a graph update pipeline.

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Commonly Asked

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Data Silo Cost Calculator

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Methodology & Sources

Scores are directional, not deterministic. Base scores draw from published benchmarks and peer-reviewed research. Parameter adjustments apply modifiers grounded in the same sources.

Primary sources listed newest to oldest. arXiv references are used in FAQ text only and do not underpin calculator scores.

Gartner D&A 2026

14% of orgs confident data is governed; 44% implemented semantic layer in 2025; Decision Governance named most underrated trend; semantic layers critical infrastructure by 2030.

Al-Sayed et al. (2025)

Ontology-grounded KG reduced hallucination from 63% to 1.7% and lifted accuracy from 37% to 98% in clinical QA (ScienceDirect/PubMed).

Lettria / AWS (2024)

KnowledgeGraphRAG 80% vs VectorRAG 50.8% accuracy across finance, healthcare, law, and industry (AWS ML Blog).

Writer / Neo4j RobustQA (2024)

GraphRAG 86% vs 59-76% for other RAG methods. NVIDIA (2024) confirmed GraphRAG excels in correctness across all metrics.

Diffbot KG-LM (2023)

3.4x accuracy improvement with Knowledge Graph vs vector-only. 0% for VectorRAG on KPI and strategic planning queries.

Corrective RAG (ICLR 2024)

Yan & Gu et al.: CRAG achieves 86.2 FactScore on Biography vs 59.2 for Standard RAG, and 74.8% on PubHealth vs 39.0%. Peer-reviewed at ICLR 2024.

Enterprise deployment ROI

Enterprise deployments report 93% query time reduction, 8 FTE savings in cross-department knowledge access, and 6-18 month ROI payback (LargitData 2025).

Full Source List

  • Sequeda, J. (2026). Gartner Data & Analytics March 2026: My Honest No-BS Takeaways. juansequeda.substack.com.
  • Al-Sayed, A. et al. (2025). Ontology-grounded knowledge graphs for mitigating hallucinations in LLMs for clinical question answering. ScienceDirect / PubMed.
  • Yan, S. & Gu, J. et al. (2024). Corrective Retrieval Augmented Generation. ICLR 2024.
  • LargitData (2025). Enterprise RAG Case Studies: Finance, Government, and Manufacturing AI Knowledge Management.
  • Various (2025). Research on the construction and application of RAG model based on knowledge graph. Nature Scientific Reports. doi:10.1038/s41598-025-21222-z.
  • Various (2025). Document GraphRAG: Knowledge Graph Enhanced RAG for Document QA. MDPI Electronics, 14(11), 2102.
  • Various (2025). GraphRAG: Leveraging Graph-Based Efficiency. ACL Anthology, GenAIK Workshop.
  • FalkorDB (2025). GraphRAG SDK Benchmark Results, Q1 2025.
  • Vectara (2025). Hallucination Leaderboard.
  • Writer / Neo4j (2024). RobustQA RAG Benchmarking Report, cited in Neo4j GraphRAG Manifesto.
  • NVIDIA (2024). Insights, Techniques, and Evaluation for LLM-Driven Knowledge Graphs. NVIDIA Technical Blog, December 2024.
  • Lettria / AWS (2024). Improving Retrieval Augmented Generation Accuracy with GraphRAG. AWS Machine Learning Blog, December 2024.
  • Gartner (2024). Knowledge graphs provide the perfect complement to LLM-based solutions. Gartner Impact Radar for Generative AI.
  • Lead Semantics (2024). Rise of the New AI Stack: GraphRAG, LLM, RAG, Knowledge Graph. leadsemantics.com.
  • Diffbot (2023). KG-LM Accuracy Benchmark.
  • Yalamanchi, P. (2020). Text to Knowledge Graph. The Startup / Medium.
  • Fatemi, B. et al. (2020). Knowledge Hypergraphs: Prediction Beyond Binary Relations. IJCAI 2020.

FAQ References (arXiv)

  • Chen, Y. et al. (2025). RAG vs. GraphRAG: A Systematic Evaluation and Key Insights. arXiv:2502.11371.
  • Han, H. et al. (2025). How Significant Are the Real Performance Gains? arXiv:2506.06331.
  • Han, H. et al. (2025). RAG vs. GraphRAG: A Systematic Evaluation. arXiv:2502.11371.
  • HyperGraphRAG (2025). Retrieval-Augmented Generation with Hypergraph-Structured Knowledge. arXiv:2503.21322.
  • Xiong, G. et al. (2025). RAG-Gym: Systematic Optimization of Language Agents for RAG. arXiv:2502.13957.
  • Chuang, Y. et al. (2024). Speculative RAG: Enhancing RAG through Drafting. Google Research. arXiv:2407.08223.
  • Rackauckas, Z. (2024). RAG-Fusion: a New Take on RAG. arXiv:2402.03367.
  • Ji, Z. et al. (2025). DeepRAG: Thinking to Retrieval Step by Step. arXiv:2502.01142.
  • Benchmarking Vector, Graph and Hybrid RAG Pipelines. arXiv:2507.03608.
  • Edge, D. et al. (2024). From Local to Global: A Graph RAG Approach. arXiv:2404.16130. Microsoft Research.
  • Gao, Y. et al. (2024). Modular RAG: Transforming RAG Systems into LEGO-like Frameworks. arXiv:2407.21059.
  • Boschin, A. et al. (2023). Neurosymbolic AI for Reasoning over Knowledge Graphs. arXiv:2302.07200. University of Edinburgh.