AI for Developers

The AI for Developers Training is a practical, hands-on program designed to help software professionals integrate AI into the software development lifecycle. The course covers AI-assisted coding, testing, debugging, refactoring, technical documentation, architecture, and secure development, enabling developers to use AI effectively while maintaining code quality, security, and engineering judgment.

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Course Includes

8 Hrs of Sessions
24/7 Lifetime Support
One-on-one doubt clearing
Hands-On Driven
Certification Based Curriculum
Career path guidance
Real Time Users
Flexible Schedules

Why Choose TM SQUARE?

Unlike generic training providers that focus on basic code completion and superficial chat prompts, TM SQUARE brings deep engineering and quality assurance expertise directly into the hands-on coding lab. Delivered by seasoned practitioners through intensive, breakout-based programming sessions, our course tackles real-world software development bottlenecks—equipping developers with production-grade prompt libraries, battle-tested refactoring workflows, and strict code-governance practices to build, test, and ship resilient software with measurable impact from day one.

Course Overview

Elevate your engineering workflow with TM SQUARE’s AI for Developers hands-on training. Designed specifically for Software Engineers, Full-Stack Developers, Tech Leads, and DevOps Engineers, this intensive 8-hour live virtual masterclass moves beyond basic code completion. Learn how to systematically integrate modern AI models into every phase of the software development lifecycle—from scaffolding idiomatic architectures and automating comprehensive test suites to diagnosing complex production bugs, accelerating code reviews, and enforcing strict enterprise security and data privacy standards

Course Outline

What is your return policy?

We offer a 30-day return policy from the date of purchase.

Do I need a receipt to return an item?

Yes, a valid receipt or proof of purchase is required.

Can I return online purchases in store?

Yes, online purchases can be returned in any of our physical stores.

How long does shipping take?

Standard shipping typically takes 3–5 business days.

Do you ship internationally?

Yes, we ship to most countries worldwide. Additional charges may apply.

Which countries are excluded?

We currently do not ship to embargoed countries or regions with postal restrictions.

Module 1: Why AI, Why Now for Developers

Icebreaker: “What tools are you already using (Copilot, Claude, ChatGPT, Cursor), and where do they fail?”

Understanding AI as an engineering pair-programmer, not an automated replacement for architecture and engineering judgment

Where AI fits across the Software Development Life Cycle (SDLC): scaffolding, implementation, testing, debugging, refactoring, and code review

What AI cannot do: understand system-wide architectural constraints, guarantee deterministic logic, or own production stability

Module 2: AI Foundations & Engineering Prompting

LLMs and code-generation models explained: context windows, token limits, and deterministic outputs

Understanding hallucinations, security vulnerabilities, and logic drifts in generated code

Prompting fundamentals for code: role-setting, framework/dependency constraints, input-output contracts, and few-shot coding examples

Live demonstration: Weak Prompt (generic snippet) vs. Strong Prompt (production-grade with type definitions, interfaces, and error handling)

Hands-on activity: Craft and iterate an engineering prompt to generate an idiomatic module adhering to strict team standards

Module 3: Scaffolding, Implementation & Idiomatic Code

Generating boilerplate, type definitions, data models, and API interfaces

Working with AI across unfamiliar languages, frameworks, or API schemas

Context management techniques: using project rules files (.cursorrules, system prompts) to enforce linters, style guides, and design patterns

AI-assisted refactoring: modernizing legacy code, optimizing database queries, and breaking down monolithic functions

Workshop: Build and integrate a production-ready micro-service or API client from a clean specification

Module 4: AI-Driven Testing & Edge-Case Exploration

Generating comprehensive unit tests and property-based tests

AI-assisted mocking, stubbing, and synthetic test-data generation

Discovering latent edge cases, boundary conditions, race conditions, and unhandled exceptions

Test-Driven Development (TDD) paired with LLMs: defining specs/tests first and generating compliant code

Workshop: Feed an existing legacy function into an LLM to uncover unhandled edge cases and generate a full test suite

Module 5: Automated Debugging, Profiling & Code Review

Root-cause analysis: parsing stack traces, memory dumps, and cryptic runtime errors with AI

Using AI as a pre-commit reviewer: spotting logic errors, anti-patterns, and suboptimal algorithms

AI-assisted performance profiling and optimization strategies

Formulating structured debugging sessions: iterative hypotheses, sanity checks, and isolation steps

Activity: Diagnose, fix, and explain a deliberately introduced subtle memory leak or race condition

Module 6: Architecture, Tech Debt & Technical Documentation

AI as an architectural sounding board: evaluating trade-offs between libraries, patterns, and design models

Drafting Architecture Decision Records (ADRs) and Request for Comments (RFCs)

Reverse-engineering undocumented codebases and creating sequence diagrams or module overviews

Generating and maintaining synchronization between code, API specifications (e.g., OpenAPI), and READMEs

Activity: Take an undocumented, legacy code module and produce an accurate ADR and technical documentation brief

Module 7: Security, Licensing & Safe AI Governance in Code

Data privacy & IP leak risks: safeguarding API keys, internal credentials, customer PII, and proprietary algorithms

Code provenance and open-source licensing risks (e.g., GPL contamination)

Identifying security vulnerabilities in AI-suggested code (OWASP Top 10, injection vectors, deserialization attacks)

Establishing human-in-the-loop validation: why every AI line requires code review and passing CI/CD pipelines

Discussion: “Where do we draw the line on AI autocompletion in critical security and infrastructure code?”

Module 8: Building Your AI-Enabled Developer Toolkit & CI/CD Workflows

Integrating AI directly into local development environments (IDEs, CLI tools, Git hooks)

Understanding Claude skills to increase productivity and consistency in repeated task

Overview of autonomous AI agents in code (e.g., agentic workflows, PR auto-reviewers, automated issue triage)

Building a personal library of reusable engineering prompts and workspace rule templates

Creating a team adoption roadmap for safe, consistent AI usage across codebases

Deliverable: Each participant leaves with an IDE configuration, personal prompt library, and a reusable workspace rules file

Session wrap-up: Q&A, feedback, and close

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