GenAI for Software Engineers

Build, Test, Debug, Secure, and Deploy Production-Ready Software with Generative AI

De: Desire Faucher

Narrado por: Virtual Voice

GenAI for Software Engineers: Build, Test, Debug, Secure, and Deploy Production-Ready Software with Generative AI

Writing code faster with AI is easy. Shipping reliable software with it is not.

You may already use generative AI to draft functions, explain unfamiliar code, or suggest fixes. But production engineering demands far more than plausible output. AI-generated changes can introduce hidden defects, weaken architecture, miss authorization rules, expose sensitive data, create unreliable tests, or make broader changes than the task requires.

This book is for software engineers, technical leads, AI engineers, and development teams who want to use coding agents and generative AI without surrendering control over correctness, security, review, and deployment.

Without a disciplined workflow, greater coding speed can simply produce defects, rework, fragile systems, and software changes that are difficult to trust.

This practical guide shows you how to place generative AI inside a controlled software engineering process that moves from specification to production evidence.

You will build a multi-tenant FastAPI and PostgreSQL application while applying a repeatable workflow:

Specify → Plan → Execute → Check → Review → Release → Observe → Improve

Through progressive, hands-on implementation, you will learn how to:

• turn requirements into executable specifications, acceptance criteria, contracts, and agent-ready task briefs

• build secure, tenant-aware application features with Python, FastAPI, PostgreSQL, SQLAlchemy, and managed migrations

• test AI-generated software using unit, integration, API, contract, property-based, mutation, security, and regression testing

• connect coding agents to approved tools and data through the Model Context Protocol

• defend against prompt injection, unsafe tool actions, secret exposure, vulnerable dependencies, and unauthorized changes

• orchestrate specialized planning, implementation, testing, and review agents with isolated branches and worktrees

• deploy and monitor GenAI-enabled applications through controlled CI/CD, containers, observability, evaluation, and governance

The guidance is designed for developers who want practical GenAI software engineering workflows rather than abstract discussion or uncontrolled automation.

The book develops one continuous production-oriented project from environment setup through architecture, implementation, authentication, authorization, testing, debugging, retrieval, MCP integration, agent orchestration, security, deployment, monitoring, and governance.

You will work with concrete engineering artifacts, including repository instructions, implementation plans, agent skills, handoff templates, verification commands, security checklists, production-readiness reviews, and evaluation scorecards. Every stage emphasizes scoped changes, executable evidence, human approval, tenant isolation, traceability, and rollback safety.

By applying these methods, you can move beyond isolated AI code generation and develop a dependable system for building software with generative AI while preserving engineering judgment and operational accountability.

Get your copy and start building AI-assisted software workflows that are faster, safer, testable, and ready for real production demands.