0: Introduction

Welcome! I’m starting a new series to show backend engineers how to integrate AI into their backend applications.

We’ll start simple, with a basic chatbot, and then layer functionality step by step until we have a production-ready AI system.

Along the way, we’ll cover the core pillars of backend engineering:

1) Core Application & AI Integration

This is where we begin – building the system itself. We’ll cover:

  • Basic LLM Integration
  • Memory Management
  • Cost & Token Controls
  • Safety & Logging
  • Streaming responses
  • Vector Retrieval (RAG)
  • Tool Calling
  • Orchestration
  • Prompting and versioning
  • Provider abstraction

The goal here is simple: Make the system work.

2) Reliability & Performance

Once the system works, the next step is making sure it behaves correctly under load and failure. We’ll cover:

  • Retries and resiliency
  • Rate limiting
  • Idempotency
  • Caching
  • Batch processing
  • Throughput and latency
  • Failure handling
  • Graceful degradation

This is where systems become production-ready.

3) Data & Distributed Systems

Modern backend systems rely heavily on data and messaging. We’ll explore:

  • Databases and schema design
  • Transactions and consistency
  • Message queues and event-driven architecture
  • Dead letter queues
  • Backpressure
  • Distributed system tradeoffs

This is where systems become scalable.

4) Observability & Operations

If you can’t see your system, you can’t run it. We’ll learn how to operate systems safely in production, including:

  • Logging and metrics
  • Distributed tracing
  • Monitoring and alerting
  • Debugging production issues
  • Incident response and postmortems

This is where engineers become operators.

5) Deployment & Infrastructure

A system isn’t real until it runs in production. We’ll cover:

  • Containerization with Docker
  • CI/CD pipelines
  • Environment configuration
  • Release strategies and rollbacks
  • Infrastructure and scaling
  • Multi-environment deployments

This is where code becomes a service.

6) Security & Production Readiness

Security is a core responsibility of backend engineers – especially in real systems. We’ll explore:

  • Authentication and authorization
  • Secrets management
  • Data protection and encryption
  • API security
  • Audit logging

This is where systems become trusted.

As you can see, AI doesn’t change the fundamentals of backend engineering. It just adds a new dependency, a new failure mode, and a new set of tradeoffs.

Everything in this series is based on real production patterns used in distributed backend systems.

Don’t worry if a lot of it doesn’t make sense yet. I’ll explain things step by step as we build.

The only prerequisites are familiarity with Java/Spring Boot, or a willingness to understand Java code and apply the same concepts in any framework or language.

The principles are what matter most.