Use Context7 CLI for up-to-date model facts (size, API format, context window) and the guidance below for "what kind of models are good for what part of a doc verification app". Consult whenever you need to pick a model for a tier slot, decide between…
Determine whether extracted entities comply with verification rules. Use after entity extraction to make the pass/fail judgment for each rule on each document. Covers translating natural language rules into executable logic, choosing between Python…
Design and calibrate confidence scoring for extraction and verification results. Use when building any workflow that needs to quantify trust in its output, when setting up quality control sampling thresholds, or when calibrating existing confidence scores…
Identify, catalog, and handle corner cases that do not fit the mainstream verification workflow. Use AFTER several rounds of skill/workflow iteration have surfaced documents that genuinely don't fit and can't be accommodated by reasonable changes to the…
Build cross-document verification rule-skills and workflows — i.e., rules where the verdict depends on facts that appear in MORE than one document. Use when authoring or distilling a rule that requires comparing entities/values across documents in a case…
Generate HTML dashboards for developer users to visualize verification results, system progress, and quality metrics. Use when a testing round completes, when production batches finish processing, when the developer user wants visual reporting, or when they…
Build intuition about document data before writing extraction logic. Use before designing any extraction schema or regex pattern, when onboarding a new document type, or when extraction accuracy is unexpectedly low and you suspect a data assumption is wrong.…
Split documents into chunks for downstream processing. Use when batching samples for observation, feeding extraction workflows, or breaking long regulation documents into pieces small enough to fit a worker LLM. Covers cheap methods (page, fixed-size,…