A checklist earns its place by catching the failures you would otherwise discover in production. This one is built for knowledge-graph extraction, organized by stage—schema, prompt, processing, validation, and operations—so you can walk a pipeline from design to deployment and confirm nothing important is missing. Each item comes with a one-line justification, because a checklist you do not understand is a checklist you will skip.
Use it two ways. Before you scale a new extraction project, run the whole list as a design review. After any change to your prompt or pipeline, rerun the relevant section as a regression check. The items are deliberately concrete enough to answer yes or no; an item you cannot answer cleanly is itself a finding.
Nothing here is exotic. These are the checks that separate extraction you can trust from extraction that merely runs, and they apply whether you are mapping contracts, papers, or news.
Schema Readiness
Is the Schema Closed and Defined
Confirm you have a finite list of entity and relation types, each with a one-line operational definition. A closed, defined schema is the foundation of consistency; without it the model invents labels and the graph fragments. This is the recurring lesson of Schema-First Habits That Keep Extracted Graphs Trustworthy.
Is the Schema Scoped to Real Questions
Verify every type in the schema serves a question the graph must answer. Extraneous types dilute the model's attention and bloat the prompt. Scope tightly; expand only when a real query demands it.
Is There an Escape Hatch for Edge Cases
Check that the schema has a way to flag entities or relations that almost fit, rather than forcing them into the wrong bucket. Flagged edge cases let you extend the schema deliberately instead of corrupting the graph silently.
Prompt Construction
Does the Prompt Include a Grounding Rule
Confirm the prompt instructs the model to extract only facts stated in the text and to omit anything requiring inference. The grounding rule is your primary defense against fabricated edges.
Does It Require Source Spans
Verify every triple must carry the exact supporting text. Source spans discourage fabrication and make verification mechanical—the cheapest insurance you can buy.
Is the Output Contract Strict and Parseable
Check that the prompt demands exact JSON with named fields and nothing else. A strict contract is what lets you automate the pipeline instead of writing endless cleanup, as the failure analysis in Why Graph Extraction Prompts Silently Drop Half Your Entities makes clear.
Is There at Least One Worked Example
Confirm the prompt includes an input-output pair exercising every field. A concrete example teaches format more reliably than prose description.
Document Processing
Are Documents Cleaned of Boilerplate
Verify navigation, ads, and repeated headers are stripped before extraction. Boilerplate wastes context and produces junk triples.
Do Chunks Overlap
Check that long documents are split with a sentence or two of overlap. Overlap prevents relationships described across a boundary from vanishing, a silent recall loss otherwise.
Is Provenance Recorded
Confirm every triple carries its source document and chunk. Provenance enables verification and conflict resolution, and is impossible to add after the fact.
Validation
Is Output Parsed and Schema-Checked on Receipt
Verify each response is parsed and validated against the schema immediately. Failing fast keeps corrupt data out of the graph and surfaces drift at once.
Are Source Spans Verified on a Sample
Check that you spot-check triples against their spans. This catches systematic errors that aggregate counts hide, a habit drawn from the end-to-end case study.
Is There a Gold-Standard Set With Precision and Recall
Confirm you measure both metrics against hand-labeled documents. Without measurement you ship a graph of unknown quality.
Operations and Iteration
Is Entity Resolution a Defined Step
Verify you have a plan to canonicalize names and merge variants, ideally against a reference list. Duplicate entities are guaranteed at scale and quietly halve a graph's usefulness.
Do You Iterate on One Variable at a Time
Check that prompt changes are isolated and remeasured. One change per iteration tells you what helped; batched changes tell you nothing, the discipline reinforced in Walk Text Through a Triple-Producing Extraction Pipeline.
Is There a Regression Set of Past Failures
Confirm fixed failures are kept as test cases. Tuning reintroduces old bugs; a regression set catches them before they ship again.
Pre-Launch and Post-Launch Gates
Have You Run the Full List as a Design Review
Before a single document is processed at scale, confirm every section above has been answered cleanly. A design review at this point is cheap; discovering a missing grounding rule after loading ten thousand documents is not. Treat the full pass as a gate the project must clear before scaling, the same hard-won sequencing reflected in the reusable extraction framework.
Have You Set Conflict-Resolution Rules
Confirm you have decided what happens when two sources assert contradictory facts—which wins, or whether both are kept with provenance. Conflicts are inevitable at scale, and a graph with no resolution policy silently accumulates contradictions that corrupt query results.
Do You Monitor Quality After Launch
Verify that precision and recall are checked periodically against the gold set even after launch, not just once before. Source documents change, edge cases appear, and prompt behavior can drift with model updates. Ongoing monitoring is what keeps a graph trustworthy over time rather than only on launch day.
Cost and Performance Checks
Have You Estimated Per-Document Cost
Confirm you know the token cost of processing one document end to end, including retries. At a few thousand documents this is a footnote; at a few million it dominates the budget. Knowing the unit cost early lets you decide whether to tighten the prompt, shorten chunks, or batch requests before the bill surprises you.
Have You Set a Retry and Failure Policy
Verify you have decided what happens when a chunk fails to parse or the model times out—how many retries, with what reminder, and where failures are logged for review. A pipeline without an explicit failure policy either silently drops data or stalls, and both are worse than a deliberate, logged decision.
Are Rare but Critical Relations Tested
Confirm your gold set includes examples of relations that appear infrequently but matter, not just the common ones. A graph can show strong aggregate precision while completely missing a rare, high-value relationship, because averages hide what is rare. Deliberately seed your tests with the edges you most need to get right, the same emphasis on critical cases found in Three Real Extraction Jobs, From Contracts to Clinical Notes.
Frequently Asked Questions
How do I use this checklist effectively?
Run the whole list as a design review before scaling a new project, then rerun the relevant section after any prompt or pipeline change. Treat any item you cannot answer cleanly as a finding to resolve, not a box to skip.
Which items matter most if I am short on time?
The closed schema, the grounding rule with source spans, and a gold-standard set with precision and recall. These three address the most common and most damaging failures. The rest refine quality once these are in place.
Why is provenance on the checklist?
Because you cannot add it retroactively. Recording each triple's source document and chunk at extraction time is what lets you verify edges and resolve conflicts later. Skip it and those capabilities are gone for good.
Do I need an escape hatch in the schema?
It is strongly recommended. A flag for near-fit entities and relations surfaces edge cases so you can extend the schema deliberately, instead of the model silently forcing them into wrong buckets and corrupting the graph.
How often should I rerun the checklist?
Run the full list before any scale-up and the relevant section after every change to the prompt, schema, or processing logic. Extraction quality drifts with changes, so periodic regression checks keep it honest.
Is this checklist specific to a domain?
No. The items apply to any extraction project—legal, biomedical, business, or otherwise. Only the schema content changes by domain; the verification disciplines are universal.
Key Takeaways
- A checklist earns its place by catching failures before production; each item here carries a one-line justification so you understand why it matters.
- Schema readiness means a closed, defined, tightly scoped vocabulary with an escape hatch for edge cases.
- Prompt construction must include a grounding rule, required source spans, a strict output contract, and a worked example.
- Document processing requires boilerplate removal, overlapping chunks, and recorded provenance.
- Validation means parsing and schema-checking on receipt, spot-checking spans, and measuring precision and recall against a gold set.
- Operations require a defined entity-resolution step, one-variable iteration, and a regression set of past failures.