Quality Engineering

← Previous Chapter: Part 4: Active RAG & Strict Tool Calling | Series Hub | Next Chapter: Part 6: Production Operations & Semantic Caching → Prerequisite: Review Part 4: Active RAG & Strict Tool Calling: Connecting LLMs to Real-Time Inventory APIs for live microservice data injection. Answer-first: The self-reflection critique loop deploys a dual-tier verification architecture combining sub-millisecond deterministic Golang constraint validators with LLM semantic reflection, slashing catalog hallucination rates below 0.05%. When candidate products violate user price ceilings or technical specifications, autonomous re-search triggers reformulate payload filters within a bounded two-iteration recursion ceiling, guaranteeing response accuracy without breaching the 200ms interactive user SLA. ...

Series Navigation: This is Part 8 (Final Chapter) of the Core Banking Systems Architecture Masterclass. ← Previous: Part 7 — Streaming Fraud Detection | Master Curriculum Hub | Curated Reading Map | Architecture Consulting Services Part 8: QA & SDET Handbook: Testing Distributed Core Banking Answer-first: Testing distributed core banking engines requires moving far beyond conventional mock-driven unit tests. By combining deterministic virtual-time concurrency testing with Go testing/synctest, automated ledger invariant property fuzzing, Jepsen split-brain chaos injection, and Envoy shadow traffic replay, financial SDETs mathematically guarantee strict linearizability, eliminate silent balance drift, and ensure continuous availability during catastrophic infrastructure network partitions. ...

Answer-first: Relying solely on foundation models for code review produces noisy, non-deterministic commentary that frustrates developers. A production AI Code Review Pipeline integrates deterministic AST linters (Semgrep) for syntax invariants with a Multi-Agent LLM-as-a-Judge consensus tier emitting standardized SARIF (Static Analysis Results Interchange Format) reports, slashing Pull Request review lead times from 28.4 hours to 2.1 hours. 1. Probabilistic vs Deterministic Code Review When organizations naively deploy a prompt-based AI review bot (e.g., “Review this git diff and list all bugs”), the bot generates dozens of pedantic comments on stylistic preferences while completely missing critical race conditions or SQL injection vulnerabilities. ...