Two Approaches for Developers to Create Evidence-Based Responses: RAG and GraphRAG

Large Language Models (LLMs) are intelligent, but they tend to produce plausible sounding answers even when uncertain, which can be problematic in development environments where precise and factual responses are necessary.

To address this, one commonly used method is Retrieval-Augmented Generation (RAG). Recently, GraphRAG has gained attention for its ability to handle relationships more effectively.

What is RAG?

RAG is a technique that involves searching external documents to attach supporting evidence before generating a response.

Workflow (Developer's Perspective)

1. Break down documents into smaller chunks.
2. Convert chunks into embedding vectors and store them (e.g., in a vector database).
3. Embed the user's query and search for similar chunks.
4. Insert the retrieved chunks into a prompt for the LLM to generate a response.

When RAG Excels

• Q&A based on company wikis, policies, and manuals.
• Summarizing from release notes or incident reports.
• Providing up-to-date answers without retraining the model.

Limitations of RAG

• Difficulty in deeply inferring relationships (cause-effect, dependencies, user-permission structures).
• Challenges with complex queries like "A is a subset of B, and B is linked to C."
• Inadequate context when related chunks are dispersed.

What is GraphRAG?

GraphRAG enhances RAG's "search" by integrating graph-based exploration. It extracts entities (e.g., services, tables, APIs, stakeholders) and their relationships (e.g., calls, ownerships, dependencies) from documents to create a knowledge graph, enabling efficient evidence gathering based on the query.

When GraphRAG Shines

• Microservice dependencies: "Where does the impact spread when an outage occurs?"
• Data lineage: "Which ETL and tables contributed to this dashboard metric?"
• Authorization/role relationships: "Who must approve this function and what policy is required?"
• Root cause analysis: "What files/modules were modified in the recent deployment?"

RAG vs. GraphRAG: A Quick Comparison

• RAG: Strong in finding nearby document chunks.
• GraphRAG: Strong in inferring long-chain knowledge with complex relationships.

Understanding Through a Simple Example

Scenario: Payment Failure Inquiry

• Question: "Why have payment failures increased since yesterday?" RAG efficiently identifies relevant incident report chunks using keywords like "payment failure," "error code," and "yesterday." Yet, if the root cause involves a chaindependency issue such as Payment API → Authentication Service → External PG, information could be scattered across documents/reports/runbooks.

GraphRAG allows narrowing down possible causes by examining service dependencies, recently altered nodes, and failure-prone paths, assembling an answer based on relationship-driven evidence.

Practical Implementation Ideas for Developers

Basic RAG Implementation (Python Example)

Below is a framework for "document chunk retrieval → using top k as context." (Replace embedding/LLM call sections to suit your SDK.)

from dataclasses import dataclass
from typing import List, Tuple
import numpy as np

@dataclass
class Chunk:
    id: str
    text: str
    vec: np.ndarray

def cosine(a: np.ndarray, b: np.ndarray) -> float:
    return float(a @ b) / (np.linalg.norm(a) * np.linalg.norm(b) + 1e-9)

def retrieve(chunks: List[Chunk], query_vec: np.ndarray, k: int = 4) -> List[Tuple[float, Chunk]]:
    scored = [(cosine(c.vec, query_vec), c) for c in chunks]
    scored.sort(key=lambda x: x[0], reverse=True)
    return scored[:k]

def build_context(topk: List[Tuple[float, Chunk]]) -> str:
    return "\n\n".join([f"[{c.id}]\n{c.text}" for _, c in topk])

# query_vec = embed("question text")
# topk = retrieve(chunks, query_vec, k=4)

GraphRAG: Building the Graph is Key

In GraphRAG, the complexity lies not in model calls but in the graph construction.

• Node examples: services, APIs, DB tables, batch jobs, teams, policy documents
• Edge examples: calls, stores, owns, creates, affects Rather than striving for a perfect knowledge graph from the start, define minimal nodes/relationships based on the 20 most frequently asked questions to improve success rates.

Which to Choose?

When to Start with RAG

• Documents are well-organized with a "find and summarize" nature.
• Quick MVP deployment within the organization is needed.
• Queries are straightforward and require little relational inference.

When to Consider GraphRAG

• There are many "why/how" questions with answers spanning multiple systems.
• Domains like operations/outages/data lineage where connective structure is knowledge.
• Simple similarity searches often hit bottlenecks after a few steps.

Conclusion: In Development, "Evidence" is the Asset

RAG strengthens the ability to retrieve knowledge, while GraphRAG enhances the capability to connect knowledge. Instead of relying solely on one, start with RAG to quickly create value (search + response), and integrate graphs when relationship inference becomes evidently necessary. Ultimately, the goal is consistent production of evidence-based answers that the team can trust.