GraphRAG Interview Questions: Graph Construction, Retrieval, Context, and Evaluation

GraphRAG interview questions test whether you can model relationships, build a retrieval pipeline, and prove that a graph actually helps the answer. Microsoft's GraphRAG documentation describes an indexing pipeline that extracts entities, relationships, and claims, builds communities, creates summaries, and supports local, global, DRIFT, and basic search modes. A strong candidate can also explain when plain vector retrieval is cheaper or better. The hard part is not drawing a graph; it is controlling extraction errors, freshness, context size, and evaluation.

Quick answer

Question	What the evidence supports	Practical move
What does GraphRAG add?	It structures a corpus as entities, relationships, communities, and summaries that can support multi-hop or global questions.	Compare it with a vector baseline on the target queries.
How is the graph built?	An indexing pipeline extracts graph elements from text and clusters or summarizes them.	Track provenance and extraction confidence.
Local versus global search?	Local search follows an entity neighborhood; global search uses higher-level community summaries.	Route queries by information need.
What is the main risk?	Extraction cost, stale relationships, noisy entities, and oversized context can offset retrieval gains.	Measure quality, latency, and refresh cost together.
How do you evaluate it?	Use task-level answer quality, retrieval evidence, faithfulness, cost, and latency.	Include a plain-RAG baseline and ablations.

Read this article in three layers. An official provider or employer source establishes what the product or process says. A candidate report can suggest a useful preparation drill, but it does not prove that every candidate sees the same configuration. The final layer is an inference: a tactic that follows from the evidence. Keeping those layers separate prevents a familiar platform name from turning into an invented universal rule.

Graph construction is an information-extraction system

Microsoft's documentation describes slicing documents into TextUnits, extracting entities, relationships, and claims, then building community structure and summaries. Each step can introduce errors: a missed alias creates disconnected nodes, a hallucinated relation creates a false path, and an incorrect community summary can contaminate many answers. Store source spans and provenance with graph elements. An interview answer should say how extraction is validated, versioned, and refreshed instead of treating an LLM-generated graph as ground truth.

Choose retrieval for the question

Local search is useful when the question starts from a known entity and needs neighboring facts. Global search uses community summaries for questions about themes or a whole corpus. DRIFT can expand from an entity while using higher-level context, and basic search provides a vector baseline. Route by query type or run a small hybrid plan. If a simple top-k chunk retriever answers the question with lower cost and equal faithfulness, the graph is unnecessary complexity.

Context packing and citations

A graph traversal can return many related nodes, but the model still has a finite context and can be distracted by weak edges. Rank evidence by relation confidence, source authority, recency, and path length. Include the source text or citation for each claim, not only a node label. Deduplicate repeated summaries and separate facts from inferred connections. The interviewer should hear how you keep a multi-hop answer traceable to documents.

Freshness and graph maintenance

A document update can change an entity, relationship, community, or summary. Choose incremental extraction for local changes, periodic rebuilds, or a hybrid that marks stale neighborhoods. Track index version and document timestamp at query time. If a deletion must remove a fact, design tombstones and re-summarization; do not leave an old community summary in the prompt. Maintenance cost is part of the architecture, especially for fast-changing operational data.

Evaluation beyond a single answer

Build a query set labeled by question type: local lookup, multi-hop synthesis, global theme, temporal change, and unanswerable request. Compare GraphRAG with vector, hybrid, and no-retrieval baselines. Measure answer correctness, evidence coverage, faithfulness, latency, token cost, index cost, and stale-answer rate. An average score can hide regressions in rare but high-value query classes. Use ablations to show whether the graph, community summaries, or prompt changes produced the gain.

Operational design and safety

Indexing can consume substantial model and storage resources. Isolate private data, enforce document-level permissions during retrieval, and avoid returning a graph path that crosses access boundaries. Log query route, index version, evidence IDs, and latency without storing sensitive text unnecessarily. Provide a fallback to basic search when graph services are unavailable. A production GraphRAG answer is a controlled data product, not just a clever prompt.

A focused preparation and recovery plan

Step	What to do	Evidence to keep
1. Confirm	Read the invitation, role page, or assessment landing page. Record the deadline, timezone, timer, allowed tools, identity checks, and accommodation contact.	A screenshot or saved message with private details redacted.
2. Baseline	Complete one representative exercise without changing the rules. Measure correctness, time, and the point where you became uncertain.	The prompt, your approach, and a short error log.
3. Diagnose	Separate a content problem from a platform, network, account, or evidence problem. Do not refresh repeatedly while a timer is running.	Timestamped notes, browser version, and visible error text.
4. Rehearse	Repeat the narrow skill this article identifies. Add one adversarial case and one explanation of what would change at larger scale.	A corrected solution or one-page decision record.
5. Escalate	If the official owner must change a deadline, reopen a session, or explain a report, send a concise factual request.	The original invite, incident timeline, and requested remedy.

Frequently asked questions

Is GraphRAG the same as vector RAG?

No. GraphRAG adds structured entities, relationships, communities, and retrieval modes; it can still use embeddings and a vector baseline.

When should you avoid GraphRAG?

When the corpus is small, relationships add little value, or the indexing and refresh cost is not justified by query quality.

How do you prevent graph hallucinations?

Store provenance, validate extraction, rank evidence, and cite source text for each generated claim.

What is local search?

It retrieves a focused entity neighborhood and associated text for a specific question.

What is global search?

It uses higher-level community summaries to reason across a broader corpus.

Before you start

Turn the article into a small decision record before you act. Write the exact question you need to answer, the source that could answer it, and the consequence of being wrong. For an assessment, that might be whether the timer stops, whether a second display is allowed, or whether a previous result can be reused. For a technical interview, it might be whether the system promises per-key ordering, whether the verifier can prove a loop is bounded, or whether a cache update can be replayed. This framing keeps preparation tied to the target rather than to a list of fashionable keywords.

Next, separate hard constraints from choices. A deadline, access rule, memory limit, privacy boundary, or source-of-truth requirement is a constraint. A page size, data structure, model pair, retry policy, or practice order is a choice that you should justify. Write one default choice, one alternative, and the signal that would make you switch. Interviewers and recruiters can then see judgment instead of a memorized answer.

Finally, decide what evidence will close the loop. Save a confirmation, a source position, a benchmark row, a test result, a metric, or a recovery ticket. Do not collect private assessment content or credentials. The evidence should be sufficient for the next person to reproduce the decision without exposing information they are not authorized to see. If the evidence is missing, label the conclusion provisional and ask the owner for the exact next action. That discipline is useful whether the page is about a hiring platform, a production system, or a candidate-reported study.

A final quality check is simple: remove any claim that depends on an unnamed company policy, an undated forum anecdote, or a number whose denominator you cannot explain. Replace it with the narrower fact, the verification step, or the limitation. Readers usually need a safe next move more than a confident-sounding prediction. That editorial boundary also makes the draft easier to update when a platform changes its controls or a new hiring cycle starts. Use the same discipline for every source and every handoff.

How to decide what to trust

The live invitation and the instructions shown inside the assessment outrank a generic article. The provider's current help center explains default product behavior, but employers can choose different question types, timers, proctoring controls, report fields, or deadlines. An official employer page establishes the published role or event context; it does not establish every team's interview sequence. Candidate reports are useful when they identify the role, date, and product, yet they remain samples. When two sources disagree, record the conflict instead of averaging it into a made-up number. If a technical incident is involved, preserve the state first, then use the platform's support route or the recruiter who owns the invitation. This approach is slower than guessing, but it gives the reader an action that can be defended and repeated.

Final takeaway

Treat GraphRAG as a measurable retrieval system: define the query class, preserve provenance, control context and permissions, and compare against a simpler baseline.

Sources and Further Reading

• Microsoft GraphRAG
• GraphRAG indexing overview
• GraphRAG query overview
• GraphRAG research paper

Research note: Sources checked 2026-09-06. Platform settings, recruiting schedules, and technical documentation can change; verify the exact invitation or current release before acting.