What is the difference between RAG and GraphRAG for enterprise LLM systems

RAG retrieves documents by similarity. GraphRAG traverses relationships in a knowledge graph. GraphRAG is better when entities and relationships matter. RAG is better when documents are self-contained.

What are the numeric performance targets for enterprise LLM systems

p95 response latency should be under 2 seconds. Unverifiable claim rate should be under 5 percent. Provenance coverage should be above 80 percent of answers citing a source.

When should an enterprise LLM system refuse to answer

If provenance coverage is below 80 percent, the system should refuse or request clarification. If source freshness exceeds 24 hours for operational data, the system must re-fetch or invalidate cache.

What breaks enterprise LLM systems in production

Common failure modes include treating RAG as a drop-in solution, ignoring governance and provenance requirements, using fine-tuning without evaluation frameworks, and deploying tool-use patterns without access controls.

Enterprise LLM Foundation

Enterprise LLM foundation is the architectural pattern for deploying large language models in production with governance, provenance tracking, and structured semantic context. It moves beyond "just add RAG" to systems that can trace answers, enforce access controls, and maintain consistency at scale. Enterprise LLM systems must answer three questions for every response: where did this come from, who can access it, and how fresh is it.

Definition: Enterprise LLM Foundation

Enterprise LLM foundation is the architectural pattern for deploying large language models in production with governance, provenance tracking, and structured semantic context. Unlike consumer LLM applications, enterprise systems must enforce access controls, track data sources, and maintain consistency across queries.

This foundation requires semantic layers, knowledge graphs, data virtualization, and performance caching as prerequisites. Without these, LLM systems become black boxes that cannot be trusted in enterprise contexts.

Mechanism: Why "Just Add RAG" Fails

Retrieval-Augmented Generation (RAG) retrieves documents by similarity and injects them as context into LLM prompts. This works for simple use cases but fails in enterprise contexts for predictable reasons.

RAG fails when:

Answers require relationships between entities, not just document similarity
Provenance must be tracked to specific sources or knowledge graph nodes
Access controls must be enforced at the entity or relationship level
Freshness requirements vary by data source and query type
Answers must be consistent across multiple queries about the same entity

Enterprise LLM systems need GraphRAG, fine-tuning, or tool-use patterns depending on the use case. RAG alone cannot provide the governance, provenance, and consistency required for production.

The mechanism failure occurs because RAG treats context as documents, not as structured entities with relationships. Enterprise systems need to reason about entities, not just retrieve text.

Decision Table: RAG vs GraphRAG vs Fine-tuning vs Tool-use

Use this decision logic to choose the right pattern for your enterprise LLM system.

Pattern	When to Use	Failure Modes	Governance Burden	Evaluation Difficulty
RAG	Self-contained documents, simple retrieval, low governance requirements	Cannot track provenance to entities, weak access controls, inconsistent answers	Low	Medium
GraphRAG	Entity relationships matter, structured knowledge, provenance required	Knowledge graph must be complete and current, traversal logic must be correct	High	High
Fine-tuning	Domain-specific terminology, consistent style, limited context windows	Hallucination increases, evaluation is expensive, models become stale	Medium	Very High
Tool-use	Real-time data access, external API integration, operational workflows	Tool failures break answers, access control complexity, latency variability	Very High	High

Most enterprise systems require a combination of patterns. GraphRAG for entity relationships, tool-use for operational data, and selective fine-tuning for domain-specific language.

Operational Implications: Governance, Provenance, and Freshness

Enterprise LLM systems operate under constraints that consumer LLM systems do not. Governance, provenance, and freshness are not optional.

Governance requirements:

Access controls must be enforced at the entity, relationship, or field level
Audit logs must track which users accessed which data through LLM queries
Data retention policies must apply to LLM-generated responses
Compliance requirements must be enforced in real-time, not retroactively

Provenance requirements:

Every answer must cite specific sources: knowledge graph nodes, documents, or API responses
Provenance must be stored and queryable, not just displayed
Source freshness must be tracked and displayed to users
Confidence scores must reflect source quality and recency

Freshness requirements:

Operational data sources must be queried in real-time or cached with short TTLs
Reference data can be cached longer but must have explicit invalidation rules
Staleness thresholds must be defined per data source and query type
Systems must re-fetch or invalidate cache when freshness exceeds thresholds

These requirements make enterprise LLM systems more complex but also more trustworthy. Governance, provenance, and freshness are the foundations of trust in production LLM systems.

Performance Targets and Thresholds

Enterprise LLM systems must meet numeric targets for latency, accuracy, and provenance coverage.

Metric	Target	Why it matters
p50 latency	< 800 ms	Common queries must be fast
p95 latency	< 2 seconds	Enterprise acceptable threshold
p99 latency	< 4 seconds	High-load stability
Unverifiable claim rate	< 5%	Claims without source trace
Hallucination rate	< 2%	Factually incorrect claims
Provenance coverage	> 80%	Answers must cite sources
Operational data freshness	< 24 hours	Real-time accuracy required
Reference data freshness	< 7 days	Acceptable staleness threshold

If these targets are not met, the system must refuse to answer, request clarification, or degrade gracefully with explicit warnings about data quality.

Gating Rules: When Systems Must Refuse or Re-fetch

Enterprise LLM systems must implement gating rules that prevent unreliable answers from being served.

If provenance coverage is below 80 percent:

The system must refuse to answer OR request clarification from the user
The refusal must explain why: "I cannot answer this question because I cannot verify the information from available sources."
The system must log the refusal and the query for review

If source freshness exceeds threshold:

For operational data older than 24 hours: the system must re-fetch from source OR invalidate cache and re-fetch
For reference data older than 7 days: the system must re-fetch OR display a staleness warning
The system must not serve answers from stale sources without explicit warnings

If access control check fails:

The system must refuse to answer and explain: "I cannot access this information due to access restrictions."
The system must not reveal that the information exists but is restricted
The system must log the access denial for audit purposes

Gating rules are non-negotiable. Systems that serve unreliable answers without gating rules cannot be trusted in enterprise contexts.

Checklist: Minimum Viable Enterprise LLM Architecture

Implement semantic layer or knowledge graph for structured entity access
Choose retrieval pattern (RAG, GraphRAG, fine-tuning, or tool-use) based on use case analysis
Implement provenance tracking: every answer must cite sources
Implement access controls at entity, relationship, or field level
Define freshness thresholds per data source and implement re-fetch logic
Implement gating rules: refuse answers below provenance coverage threshold
Add performance caching for entity resolution and relationship traversal
Implement data virtualization for unified access to distributed sources
Set up monitoring: track latency, provenance coverage, and unverifiable claim rate
Define evaluation framework: how to measure accuracy and governance compliance
Implement audit logging: track which users access which data through LLM queries
Establish fallback behavior: what happens when sources fail or thresholds are exceeded

Skipping any step leads to systems that cannot be trusted in production. Enterprise LLM foundation requires all components, not just LLM APIs and RAG.

Failure Modes: What Breaks in Production

Enterprise LLM systems fail in production for predictable reasons. Most failures are architectural, not model-related.

Common failure modes:

Treating RAG as a drop-in solution: RAG works for simple use cases but fails when relationships, provenance, or access controls are required. Systems that use RAG without considering alternatives break when governance requirements emerge.
Ignoring governance and provenance: Systems that do not track where answers come from cannot be trusted. Users will discover incorrect answers with no way to verify or correct them.
Using fine-tuning without evaluation frameworks: Fine-tuned models can hallucinate more than base models. Systems that fine-tune without rigorous evaluation frameworks produce unreliable outputs.
Deploying tool-use patterns without access controls: Tool-use patterns expose enterprise systems to external APIs. Without access controls, systems can leak data or perform unauthorized operations.
Missing freshness thresholds: Systems that serve stale data without warnings produce incorrect answers. Users will lose trust when answers are outdated.
No gating rules: Systems that always provide answers, even when sources are unreliable, produce unreliable outputs. Gating rules are required to maintain trust.

What does NOT work:

Using consumer LLM APIs directly without governance layers
Relying on prompt engineering alone to enforce access controls or provenance
Deploying fine-tuned models without evaluation frameworks and monitoring
Using RAG when GraphRAG or tool-use patterns are required
Ignoring latency targets and serving slow answers without caching

Most production failures are caused by architectural gaps, not model quality. Enterprise LLM foundation addresses these gaps systematically.

FAQ

What is the difference between RAG and GraphRAG for enterprise LLM systems: RAG retrieves documents by similarity. GraphRAG traverses relationships in a knowledge graph. GraphRAG is better when entities and relationships matter. RAG is better when documents are self-contained.
What are the numeric performance targets for enterprise LLM systems: p95 response latency should be under 2 seconds. Unverifiable claim rate should be under 5 percent. Provenance coverage should be above 80 percent of answers citing a source.
When should an enterprise LLM system refuse to answer: If provenance coverage is below 80 percent, the system should refuse or request clarification. If source freshness exceeds 24 hours for operational data, the system must re-fetch or invalidate cache.
What breaks enterprise LLM systems in production: Common failure modes include treating RAG as a drop-in solution, ignoring governance and provenance requirements, using fine-tuning without evaluation frameworks, and deploying tool-use patterns without access controls.

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