AI EngineeringInterview preparation
Advanced·Concept·2 min read

Data model

Practice this question
RecordEssential fields and purpose
customersTenant, customer identity, permitted contact/profile fields; maintained by business service
policiesTenant/customer, product, region, validity, document version, endorsements; canonical applicability
claimsTenant/customer/policy, status, receipt, required documents; canonical claim state
documentsTenant, source ID/hash, MIME/size, ACL, version, parser state, publication status, timestamps
chunksTenant/document/version, section/page, text, embedding model/dimension, vector, eligible metadata
conversations/messagesTenant/user/session, bounded content and roles, tool relationships, retention category
agent_runsTenant/user, graph version, task, phase, deadline/budget, checkpoint reference, outcome
claim_draftsDraft ID/version, structured fields, source refs, content hash, creator, validation results
approvalsDraft ID/hash, action, reviewer, scope, timestamp, expiry, decision; authenticated record
operation_receiptsTenant/action/key, payload hash, pending/completed/unknown state, external reference
outboxBusiness-linked event ID, payload, dispatch status; reliable async publication
user_memoriesTenant/user, approved fact/preference, source, timestamp, expiry; separate from claim truth
audit_eventsActor, action, object/version, decision, outcome, run/receipt reference; restricted immutable history
  • Enforce unique operation keys and optimistic draft versions.
  • Keep large original files in protected object storage.
  • Keep model-visible projections small.
  • A document index and conversation cache are rebuildable derived data; claim and approval records need stronger durability.
Adapted from 13_AI.MD · “Data model”. Code examples are study fragments. Difficulty and core labels are editorial estimates; version-specific behavior should be checked against your project.