This is a working checklist, not a poster. Each item comes with a short justification so you can decide whether it applies to your situation rather than checking boxes blindly. Use it two ways: as a pre-flight before you build a knowledge graph, and as an audit for one you already run. If an item doesn't fit your context, skip it deliberately — but know why.
The checklist is organized into five phases that mirror a graph's lifecycle: scoping, modeling, ingestion, querying, and operations. Work top to bottom for a new build; jump to the relevant phase for an audit. For the reasoning behind these in depth, pair this with our best practices article — this checklist is the condensed, actionable version.
Phase 1: Scoping
Before any modeling, confirm the graph is the right tool and its purpose is sharp.
- [ ] Written three to five concrete questions the graph must answer. These are your blueprint and scope guard; without them, the model sprawls.
- [ ] Confirmed the key questions traverse three or more relationships. If they're aggregations, a warehouse is the better tool — don't pay graph complexity for nothing.
- [ ] Identified who will query the graph and how. A graph nobody can query is shelfware; know your audience's skill level now.
- [ ] Decided what success looks like, measurably. "Faster answers" is vague; "agent reaches related records in one traversal" is testable.
If you can't pass scoping, stop. A graph built without these almost always becomes the data-first sprawl described in our common mistakes guide.
Phase 2: Modeling
Design the smallest model that answers your questions.
- [ ] Derived node types from the nouns in your questions. Question-driven modeling keeps the schema lean enough to hold in your head.
- [ ] Derived edge types from the verbs, and kept the count small. Edge-type sprawl silently breaks traversals; fewer, well-named types stay reliable.
- [ ] Moved attributes to properties, not nodes. A publish date is a property of an article, not a separate node — resist node inflation.
- [ ] Chosen a light schema over a formal ontology unless the domain demands it. Formal ontologies earn their cost only in regulated, ambiguity-heavy domains.
- [ ] Verified every node and edge type serves at least one question. If it serves none, delete it — it's pure cost.
Phase 3: Ingestion and Resolution
This phase is where correctness is won or lost.
- [ ] Built entity resolution as a repeatable pipeline, not a one-time cleanup. Data keeps arriving messy; a one-shot clean-up is stale immediately.
- [ ] Tiered your matching: exact-key auto-merge, fuzzy with threshold, human queue for the uncertain. Different confidence levels deserve different handling.
- [ ] Spot-checked merges on your five most important entities. If your biggest customer is two nodes, the problem is widespread — catch it now.
- [ ] Attached provenance (source, date, confidence) to nodes and edges. You can't trace a wrong fact or cite a source you didn't record.
Resolution is the highest-stakes item on this entire checklist. Treat its sub-items as non-negotiable for any graph you'll trust.
Phase 4: Querying and Validation
Prove the graph answers the questions it was built for.
- [ ] Translated each scoping question into a working traversal. If a question can't be expressed as a query, the model is missing something.
- [ ] Built a fixed validation set of questions with known-correct answers. Graphs fail silently; only a ground-truth set turns corruption into a loud failure.
- [ ] Re-run validation after every significant change. A query returning "something" looks fine even when resolution broke.
- [ ] Confirmed query performance is acceptable for your hot paths. Deep traversals on a bloated graph slow down; measure before you ship.
The validation set is the cheapest insurance available and the most-skipped item. Don't skip it. The step-by-step guide shows how to build one.
Phase 5: Operations and Evolution
A graph is a living system; plan for its life.
- [ ] Scheduled periodic model reviews to add, rename, or retire types. A write-once graph drifts out of step with its questions and dies slowly.
- [ ] Established governance for new edge types (a review gate). Treat the edge vocabulary like an API to prevent synonym sprawl.
- [ ] Set up ongoing entity resolution for new and renamed entities. Without it, name collisions creep back and silently corrupt answers.
- [ ] Documented the live question list as the schema's specification. New element, new question — this keeps the graph lean over time.
The 2026 Additions: AI in the Loop
Two items are newly essential as large language models become standard graph tooling.
- [ ] If using an LLM to extract entities, constrained it with your controlled vocabulary. Unconstrained models invent duplicate nodes and synonym edges — AI proposes, governed rules dispose.
- [ ] If feeding the graph to an LLM for grounding, verified provenance flows through to citations. The whole point of GraphRAG is citable, sourced answers; provenance must survive the pipeline.
These reflect the bidirectional relationship between graphs and models: AI helps build the graph, and the graph helps keep AI factual. Skipping the guardrails turns that strength into a liability.
A Note on Sequencing
The phases are ordered for a reason, and skipping ahead is the most common way teams misuse a checklist like this. Scoping gates modeling because you can't design a model without questions. Modeling gates ingestion because you can't resolve entities into node types you haven't defined. Ingestion gates querying because you can't validate answers from a graph that isn't loaded correctly. And operations only makes sense once the first four phases have produced a graph worth maintaining.
If you find yourself wanting to skip forward — "we'll write the questions later, let's just load data first" — treat that urge as a warning sign. It's the exact impulse that produces data-first sprawl. The checklist's order is its advice. Work it top to bottom for a build, and only jump around when auditing a graph that already exists.
How to Use This as an Audit
For an existing graph, run the checklist as a scorecard. Two failures predict trouble more than any others: no fixed validation set (Phase 4) and no entity resolution pipeline (Phase 3). If either is unchecked, prioritize it over everything else — they're the difference between a graph that's reliably right and one that's confidently wrong. Everything else can be improved incrementally; those two affect whether your answers are true at all.
Frequently Asked Questions
Can I use this checklist for a small prototype?
Yes, but skip the operations and governance items (Phase 5) and provenance, which are overhead for throwaway work. Keep the scoping and resolution items even for prototypes — a prototype that gives wrong answers teaches you the wrong lessons. The checklist is meant to be scaled to your stakes.
Which single item matters most?
Building a fixed validation set (Phase 4). Graphs fail silently, and a ground-truth set is the only thing that reliably catches it. It's cheap to build and almost universally skipped, which makes it the highest-leverage item on the list.
How often should I re-run the audit?
After every significant model change at minimum, and on a scheduled cadence (quarterly is reasonable) for a graph in production. The operations phase exists precisely because graphs drift; periodic auditing is how you catch drift before it compounds into wrong answers.
Do the AI items apply if I'm not using LLMs?
The two 2026 additions only apply if an LLM is in your build or query pipeline. If you're constructing and querying the graph by hand or with rule-based tools, skip them. They're guardrails specifically for the failure modes LLMs introduce.
What if I fail the scoping phase?
Stop and reconsider whether you need a graph at all. Failing scoping usually means your questions are aggregations (warehouse territory) or you don't yet have concrete questions. Building past a failed scoping phase produces the data-first sprawl that dooms most projects.
Key Takeaways
- Use the checklist as both a pre-flight for new builds and an audit for existing graphs.
- Scoping confirms a graph is even the right tool — fail it and reconsider.
- Entity resolution and a fixed validation set are the two highest-stakes items; never skip them.
- Treat the graph as a living system with governed edge types and scheduled model reviews.
- Add AI-specific guardrails whenever an LLM is in your build or query pipeline.