RAG vs. Knowledge Graphs vs. Fine-Tuning: A Decision Framework

"Should we use RAG, a knowledge graph, or fine-tuning?"

This is the most common architecture question in enterprise AI. The answer is: it depends on what you're trying to achieve. Here's the framework.

The Three Approaches, Explained

Retrieval-Augmented Generation (RAG)

What it does: At query time, retrieves relevant documents from a corpus and includes them in the model's context. The model generates responses grounded in retrieved content.

Best for: Questions answerable by finding the right document passage.

Architecture: Vector database + embedding model + LLM

Knowledge Graphs

What it does: Stores entities and relationships in a structured graph. At query time, traverses the graph to find relevant facts and relationships.

Best for: Questions requiring understanding of entities, relationships, and organizational context.

Architecture: Graph database + entity extraction + LLM

Fine-Tuning

What it does: Modifies model weights using training data. The model "learns" patterns from examples.

Best for: Teaching the model new behaviors, styles, or domain-specific language.

Architecture: Base model + training data + compute resources

The Decision Matrix

Factor	RAG	Knowledge Graph	Fine-Tuning
Knowledge type	Documents	Entities/relationships	Patterns/style
Update frequency	Easy	Easy	Difficult
Accuracy on facts	Good	Excellent	Poor
Accuracy on relationships	Poor	Excellent	Poor
Entity resolution	None	Built-in	None
Interpretability	Medium	High	Low
Setup complexity	Low	Medium	Medium
Ongoing cost	Low	Medium	High for retraining

Scenario-Based Decisions

Scenario 1: "Find information in our documents"

Example: "What's our vacation policy?"

Best approach: RAG

Why: The answer exists in a document. RAG finds the document and extracts the answer. No entity resolution or relationship understanding needed.

Scenario 2: "Understand entities across our organization"

Example: "What's our relationship with Acme Corporation?"

Best approach: Knowledge Graph

Why: "Acme Corporation" might appear as "Acme Corp," "ACME," and "Vendor 4412" across systems. The answer requires aggregating information across entity representations and understanding relationships (contracts, contacts, projects).

Scenario 3: "Write like us"

Example: "Generate a product description in our brand voice"

Best approach: Fine-Tuning

Why: This is about style and pattern, not facts. Fine-tuning teaches the model to generate text that matches organizational conventions.

Scenario 4: "Understand our business context"

Example: "What should I know before my meeting with Client X?"

Best approach: Knowledge Graph (with RAG for supporting documents)

Why: This requires understanding Client X as an entity, their relationships to your organization, recent interactions, and current status—all entity/relationship questions.

Scenario 5: "Answer questions about technical documentation"

Example: "How do I configure feature X?"

Best approach: RAG

Why: The answer is in the documentation. Retrieve the relevant section, present it to the user. Document-grounded Q&A.

Scenario 6: "Understand our historical context"

Example: "Why did we make decision X last year?"

Best approach: Knowledge Graph + RAG

Why: The knowledge graph captures the decision as an entity with relationships (who made it, what it affected). RAG can retrieve the supporting documents (meeting notes, memos).

The Layered Architecture

In practice, mature enterprise AI uses all three:

Different queries engage different layers:

"What's our vacation policy?" → RAG
"Who owns the Acme relationship?" → Knowledge Graph
"Write a proposal in our style" → Fine-tuned model

The architecture routes queries appropriately.

Common Mistakes

Mistake 1: RAG for Everything

Symptom: "Our RAG system gets the Acme question wrong—it returns documents about Acme but doesn't understand the relationship."

Problem: RAG retrieves documents. It doesn't understand entities or relationships. Using RAG for entity questions produces fragmented, inaccurate answers.

Fix: Add a knowledge graph layer for entity/relationship queries.

Mistake 2: Fine-Tuning for Facts

Symptom: "We fine-tuned on our org chart but the model still says John is CFO when he left months ago."

Problem: Fine-tuning learns patterns, not facts. And patterns freeze at training time.

Fix: Use a knowledge graph for factual knowledge that changes.

Mistake 3: Knowledge Graph Without Documents

Symptom: "The knowledge graph knows Acme is our biggest customer, but when asked 'what does our contract say?' it can't help."

Problem: Knowledge graphs store structured facts, not document content.

Fix: Use RAG alongside the knowledge graph for document-grounded queries.

The Build Order

If starting from scratch, build in this order:

RAG: Get basic document Q&A working. Low complexity, immediate value.
Knowledge Graph: Add entity resolution and relationship context. This is where accuracy on organizational questions jumps.
Fine-Tuning (if needed): Only after RAG and knowledge graph are working. Fine-tune for style adaptation or domain terminology—not for facts.

Most enterprises never need fine-tuning. Many need knowledge graphs more than they need more sophisticated RAG.

Evaluating Your Current State

Assess which layers you have and which you need:

RAG Layer

Documents indexed in vector database
Query-document retrieval working
Results provided to LLM for response generation

Knowledge Graph Layer

Critical entities identified and extracted
Entity representations resolved across systems
Relationships mapped and queryable
Business rules encoded

Fine-Tuning Layer

Organizational style captured in training data
Domain terminology included
Model trained and deployed
Retraining process established

Most enterprises are heavy on the first, light on the second, and shouldn't worry about the third yet.

The Bottom Line

Use RAG when the answer is in a document. Use knowledge graphs when the answer requires understanding entities and relationships. Use fine-tuning when the answer requires organizational voice or style.

Get the layering right, and enterprise AI starts working. Get it wrong, and you'll wonder why the expensive infrastructure doesn't deliver.

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