The 54-Point Gap on Multi-Hop
Single-fact lookups are fine with Vector RAG: embed the question, pull the nearest chunk, answer. The problem is multi-hop queries that require chaining reasoning across several documents. On multi-hop benchmarks GraphRAG scores 86% while Vector RAG scores 32% — a 54-point gap. Vector search reliably finds semantically close chunks but cannot stitch together a relation chain like "A acquired B, and B's subsidiary sued C." GraphRAG, which builds entities and relations into a graph, follows those connections explicitly.
How LazyGraphRAG Lowered the Cost Barrier
GraphRAG's obstacle was indexing cost. Pre-generating community summaries across an entire corpus drives token spend up sharply. LazyGraphRAG defers those community summaries from build time to query time, summarizing only the subgraph an actual query touches. That pushes the barrier below $5 per corpus, which is what lets you keep the graph path as a standing candidate rather than a rare exception.
The Hybrid Router That Splits Three Ways
Running every query through the graph wastes latency and cost. The 2026 dominant pattern is a router that classifies query type and splits traffic semantic 80%, graph 15%, agentic 5%. Most simple lookups go to vectors, the minority needing relational reasoning go to the graph, and the tiny slice needing multi-step tool calls goes to the agentic path. Keep the router thin, and log the rationale for every branch decision.
A Full Guide: From Planning to Operations
In planning, pin routing goals to numbers. Set baselines of routing-classification accuracy at 90% or higher, semantic-path p95 latency at or below 800ms, graph-path p95 latency at or below 2.5s, and a misroute rate under 5%. Build a gold set of 200-plus queries labeled by type (single-fact, relational, multi-step) and measure pass rate as how often the router hits those labels. Track answer accuracy and cost per path — vector, graph, agentic — separately, so you can confirm that branching actually raises accuracy.
Three failure patterns are common. First, a relational query misrouted to semantic returns only nearby chunks and produces a wrong answer; when evidence coverage falls below threshold, auto-retry on the graph path. Second, when subgraph traversal explodes and graph-path latency crosses its limit, apply a safe truncation to 2 hops and mark the partial answer with an uncertainty flag. Third, when the agentic path sees tool calls fail three or more times, halt auto-improvement and route to a human-review queue. On any path, if the model would answer without grounding evidence, hold the response.
The operations checklist depends on a standard log. Record query ID, the path the router chose, branch confidence, the selection rationale, final correctness, and per-path latency and cost in a single-line schema. When a retry occurs, log both the initial and retry paths so misroute rate can be tallied after the fact. To keep PII out of the graph and the logs, apply PII masking before entity extraction, and exclude records where masking fails from indexing.
The improvement loop reviews misroutes weekly. When relational queries wrongly sent to semantic accumulate, adjust the router's decision threshold or add them to the gold set for retraining. When a query type loses its graph-over-vector accuracy edge, route that type back to vectors to save cost. Treat the split ratio (80/15/5) not as a fixed constant but as an operating metric that is continually retuned against real accuracy and cost.
Executive Summary
In short, the 86% vs 32% multi-hop gap — 54 points — is why the graph path exists, and LazyGraphRAG's query-time deferral brings cost below $5 per corpus, making it a standing candidate. Route thin: semantic 80% / graph 15% / agentic 5%, but recover from failure with a graph retry on low evidence coverage, hop truncation on latency overrun, and human review on repeated tool failure. Track misroute rate with standard logs and PII masking, and keep retuning the split ratio against accuracy and cost — that is the core of operating it.