GraphRAG & Advanced RAG Techniques: When Plain Vector Search Isn’t Enough

Basic RAG is now table stakes: embed chunks, store them in a vector database, and feed the top-k results into an LLM. But in real products—where users ask multi-hop questions, reference entities, and expect non-trivial reasoning—plain vector search starts to feel blunt. That’s where GraphRAG and other advanced RAG techniques come in.

GraphRAG treats your data as nodes and relationships, not just chunks of text. Instead of asking, “which paragraphs are similar to this query?” you can ask, “which entities and paths in the graph answer this question?” FineTune Lab helps you operate these more complex retrieval pipelines in production: monitoring their behavior, comparing them to vanilla RAG, and using real traces to fine-tune your models.

From Plain RAG to GraphRAG

If you’re new to RAG, start with the fundamentals in Vector Databases & Embeddings. At a high level, classic RAG looks like this:

• Embed documents or chunks into vectors.
• Use similarity search to retrieve top-k chunks for a query.
• Construct a prompt with those chunks and send it to the LLM.

This works well for many “find the answer in these docs” tasks. It struggles when:

• Questions involve multiple hops across documents or entities.
• You care about relationships (who reports to whom, which APIs depend on which services, etc.).
• You already have a knowledge graph, schema, or relational data that is richer than free text.

GraphRAG extends RAG by using a graph—nodes, edges, and properties—as the retrieval substrate. Instead of retrieving only chunks, you retrieve entities, relationships, and paths and then inject those into the LLM’s context.

Core Building Blocks of GraphRAG

Most GraphRAG implementations share a few core elements:

• Graph schema – node types (people, services, documents), edge types (depends_on, authored_by, cites), and properties.
• Ingestion pipeline – from raw docs or events to extracted entities and relations.
• Graph store – a database like Neo4j, RedisGraph, or an engine layered on top of Postgres or specialized graph infra.
• Graph-aware retriever – query rewriting, entity linking, and path expansion logic.
• Context construction – turning nodes, edges, and supporting text into a prompt the LLM can reason over.

For many teams, a pragmatic starting point is a hybrid stack: keep your existing vector-based RAG pipeline and add a graph side-car where it clearly wins, such as impact analysis, incident response, or complex product configuration questions.

Advanced RAG Techniques Around GraphRAG

GraphRAG is one advanced technique, but usually lives alongside others:

• Query rewriting & decomposition – use the LLM to turn a complex user question into structured sub-queries or graph patterns.
• Hybrid dense + sparse + graph retrieval – combine BM25, vector search, and graph traversals into a fused context.
• Step-wise retrieval – fetch initial nodes, then iteratively expand neighbors based on intermediate reasoning.
• Tool-augmented GraphRAG – treat the graph as a tool that agents call with structured inputs and receive structured outputs.

These patterns align naturally with agentic systems. For example, a planner agent can decide when to call the graph, which node types to target, and how far to expand, while another agent focuses on answer generation. If you are running such systems, see also Multi-Agent Systems & Agentic AI: From Hype to Reliable Operations for observability and fine-tuning guidance.

Designing a GraphRAG Pipeline

A minimal GraphRAG pipeline might look like this:

1. Entity/relationship extraction – run NER and relation extraction over your corpus; optionally use LLMs for higher-quality patterns.
2. Graph construction – insert nodes and edges into your graph store, keeping IDs that point back to original documents.
3. Query understanding – map user questions to entities and relation patterns (for example, “impact of service X failing” → neighbors in a dependency graph).
4. Graph traversal – run queries like “all nodes within 2 hops of X” or “shortest paths between A and B”.
5. Context assembly – collect relevant nodes, edges, and source passages; format into a prompt with structured sections.

In practice, you will likely mix text retrieval and graph traversal: use vectors to find candidate entities, then use the graph to expand and structure context.

Evaluation: Is GraphRAG Actually Better?

You should not adopt GraphRAG on vibes. As we cover in How to Evaluate and Benchmark RAG Pipelines, you need clear metrics that answer “is this routing and retrieval setup better for my real workloads?”

For GraphRAG, this means evaluating both:

• Retrieval quality – does the graph traversal surface the right entities, relations, and supporting docs?
• Answer quality – are answers more correct, grounded, and complete for multi-hop and relational questions?

Additional graph-specific checks include:

• Path correctness – whether returned paths actually reflect valid relationships in your domain.
• Coverage vs noise – whether expansions are too shallow (missing key nodes) or too wide (overwhelming the LLM with irrelevant neighbors).
• Stability – whether small graph changes cause large swings in behavior or explanations.

FineTune Lab helps here by treating each GraphRAG request as a trace: you can log the graph queries, selected nodes/edges, and final answers, then score them with human labels or LLM-as-a-judge. That makes it easier to compare GraphRAG variants to your baseline RAG implementation and justify the extra complexity.

Fine-Tuning for GraphRAG and Advanced RAG

Advanced RAG pipelines often rely on the LLM to perform tasks like entity linking, query decomposition, and explanation. Fine-tuning can make those steps much more reliable.

• Entity linking models – fine-tune models that map user mentions to graph nodes.
• Query planners – fine-tune the LLM to output structured graph queries or traversal plans.
• Graph-aware answerers – fine-tune on examples where the model must explicitly reference nodes, paths, and sources.

In FineTune Lab, you can collect training data for these behaviors directly from production:

• Capture traces where entity linking or graph traversal failed.
• Label the correct entities, paths, or explanations.
• Run LoRA or QLoRA fine-tunes targeted at those tasks, as outlined in LLM Fine-Tuning Best Practices & Techniques.
• Deploy and compare fine-tuned variants to see if graph usage becomes more consistent and accurate.

Operating GraphRAG in Production

GraphRAG increases your blast radius: you’re operating a graph database, a vector store, and LLMs together. To keep this sustainable:

• Instrument every stage – log graph queries, vector searches, context sizes, and LLM calls per request.
• Track unit economics – monitor latency and cost for GraphRAG vs vanilla RAG.
• Watch drift – as your graph and documents evolve, monitor answer quality and path correctness.
• Guard against over-expansion – cap hops, nodes, and tokens in graph-based contexts.

FineTune Lab provides a single place to observe these metrics and traces. You can see which queries are routed to GraphRAG, how they perform, and where targeted fine-tuning or graph curation would have the most impact.

Getting Started with GraphRAG in FineTune Lab

You don’t need a perfect graph to get value. Start small:

• Pick one domain where relationships matter (for example, service dependencies, research topics, or product features).
• Build a simple graph schema and ingestion pipeline for that domain.
• Integrate graph queries into your existing RAG stack for that slice of traffic.
• Send those traces into FineTune Lab and evaluate the impact.

Inside FineTune Lab, you can talk to Atlas, our in-app assistant, to walk through set-up: connecting your GraphRAG pipeline, defining evaluation scenarios, and turning real-world failures into fine-tuning datasets. When you are ready, you can start a free trial of FineTune Lab and begin treating GraphRAG and advanced RAG techniques as an operational capability—not just a one-off experiment.