The Enterprise AI Engineering Handbook
A comprehensive, practical field guide for CTOs, architects, and engineering leaders building, governing, and scaling production AI systems.
Handbook Scope & Architecture
The Handbook is an evolving body of knowledge covering foundational distributed systems design, LLM gateway engineering, evaluation harnesses, security perimeters, and regulatory compliance.
Enterprise AI Foundations
Core primitives: LLMs, transformers, context windows, tokenization, and enterprise constraints.
AI Strategy & Portfolio Prioritisation
Framing AI investments, defining ROI metrics, and avoiding the technology-first trap.
AI Operating Model & Team Topology
Centralized vs federated AI teams, Centers of Excellence, and skill matrix design.
Enterprise AI Architecture
System design patterns, reference architectures, and separation of concerns.
Enterprise RAG in Practice
Chunking, vector indices, hybrid retrieval, reranking, and citation generation.
Agentic AI & Workflow Orchestration
State machines, tool-use protocols, multi-agent coordination, and failure handling.
AI Platform Engineering
Building self-service internal developer platforms for GenAI capabilities.
Model Selection & Evaluation
Frontier models vs open-weight models, benchmarking latency, cost, and reasoning.
LLM Gateways & Routing Layers
Reverse-proxies, semantic caching, failover policies, and PII sanitization.
Evaluation & Ground Truth Engineering
Curating golden datasets, LLM-as-a-judge methodologies, and continuous CI evals.
LLMOps & Lifecycle Management
Prompt versioning, dataset lineage, continuous integration, and canary releases.
AI Observability & Telemetry
Distributed tracing, token accounting, latency metrics, and drift monitoring.
AI Security & Adversarial Defense
OWASP Top 10 for LLMs, prompt injection, data poisoning, and sandboxing.
AI Governance & Compliance
Operationalizing regulatory standards, model inventory, and audit reporting.
Responsible AI & Ethics
Bias detection, transparency, explainability, and algorithmic fairness.
AI FinOps & Unit Economics
Token budgeting, cost modeling, semantic cache ROI, and model distillation.
AI-Enabled SDLC
CLI coding agents, automated PR reviews, unit test synthesis, and developer onboarding.
Measuring Developer Productivity with AI
Beyond lines of code: DORA metrics, cycle time, cognitive load, and review velocity.
Human-in-the-Loop Architecture
Approval workflows, exception queues, active learning, and escalation design.
Production Readiness Checklist
The 50-point engineering and security verification gate before public launch.
Build vs Buy for Enterprise AI
Evaluating SaaS platforms, vendor lock-in, open source stacks, and total cost of ownership.
Enterprise Adoption & Change Management
Overcoming organizational resistance, executive storytelling, and skill development.
AI in Financial Services
Banking use cases, APRA CPS 234 compliance, payments, and risk management.