Gemini for End-To-End SDLC (Software Development Lifecycle) Course

Lesson 1 • What Gemini Is and Does

Covers Gemini's architecture, modalities, and model variants. Grounds learners in what the tool can and cannot do before applying it to development tasks.

Lesson 2 • Accessing Gemini in Your Workflow

Demonstrates access methods including Gemini Advanced, API, and IDE integrations. Learners configure their environment for hands-on exercises.

Lesson 3 • Prompt Engineering Fundamentals

Introduces structured prompting techniques essential for reliable outputs. These patterns are reused in every subsequent chapter.

Lesson 1 • Requirements Review and Validation

Uses Gemini to detect conflicts, redundancies, and missing constraints in requirement sets. Prepares artifacts for stakeholder sign-off.

Lesson 2 • Eliciting Requirements Using AI

Uses Gemini to generate interview questions, stakeholder scenarios, and gap analyses. Connects AI-assisted elicitation to higher-quality requirement coverage.

Lesson 3 • Writing User Stories and Acceptance Criteria

Generates and refines user stories in standard formats with testable acceptance criteria. Ensures stories are sprint-ready before handoff to development.

Lesson 1 • Translating Requirements into Architecture

Converts requirement documents into candidate architecture patterns using Gemini. Bridges the gap between business needs and technical structure.

Lesson 2 • Design Review and Risk Identification

Prompts Gemini to critique architecture drafts for scalability, security, and maintainability risks. Produces a prioritized risk register for the design phase.

Lesson 3 • Architecture Decision Records

Generates structured ADRs capturing context, options, and rationale for key decisions. Creates a durable design history that supports future maintenance.

Lesson 4 • Generating System and Sequence Diagrams

Produces Mermaid and PlantUML diagram code from natural language descriptions. Enables rapid visualization of system interactions and data flows.

Lesson 1 • Code Explanation and Documentation

Uses Gemini to explain unfamiliar code and auto-generate inline and API documentation. Reduces onboarding time and improves codebase maintainability.

Lesson 2 • Multi-File and Multi-Module Code Tasks

Manages context across multiple files to generate cohesive, cross-module code. Addresses context window limitations with chunking strategies.

Lesson 3 • Code Generation Prompting Strategies

Applies advanced prompting patterns specifically for code output quality and consistency. Establishes reusable templates for common coding tasks.

Lesson 4 • Working with Databases and Data Models

Generates schema definitions, migrations, and query logic from data model descriptions. Covers both SQL and NoSQL patterns.

Lesson 1 • Automated Code Review Workflows

Configures Gemini to review pull requests for bugs, style violations, and logic errors. Integrates AI review into existing CI/CD pipelines.

Lesson 2 • Identifying and Resolving Code Smells

Detects common code smells including duplication, long methods, and tight coupling. Generates refactoring plans with before-and-after code examples.

Lesson 3 • Security Vulnerability Detection

Scans code for injection flaws, insecure dependencies, and authentication weaknesses. Produces remediation suggestions ranked by severity.

Lesson 1 • Performance and Load Test Planning

Generates performance test plans, load profiles, and threshold definitions from system specs. Connects test design to non-functional requirements.

Lesson 2 • Test Coverage Analysis and Gap Filling

Analyzes existing test suites to identify uncovered branches and missing scenarios. Generates targeted tests to close coverage gaps.

Lesson 3 • Integration and End-to-End Test Design

Designs integration test scenarios covering service boundaries and data flows. Extends to end-to-end test scripts for critical user journeys.

Lesson 4 • Bug Report Analysis and Reproduction

Uses Gemini to parse bug reports, hypothesize root causes, and generate reproduction steps. Accelerates triage and reduces time to fix.

Lesson 1 • Containerization and Orchestration

Generates Dockerfiles, Compose files, and Kubernetes manifests from service specifications. Covers resource limits, health checks, and scaling policies.

Lesson 2 • Observability and Alerting Setup

Creates logging configurations, metric dashboards, and alert rules using Gemini. Connects observability design to SLOs defined in requirements.

Lesson 3 • Infrastructure as Code Generation

Generates Terraform, Bicep, and CloudFormation templates from architecture descriptions. Produces deployment-ready IaC with security best practices embedded.

Lesson 4 • Incident Response and Postmortem Support

Uses Gemini to analyze logs, suggest root causes, and draft postmortem reports. Reduces mean time to resolution and improves team learning.

Lesson 1 • Measuring and Improving AI-Augmented Teams

Defines KPIs for AI adoption including velocity, defect rate, and review cycle time. Uses data to iterate on prompt strategies and tooling choices.

Lesson 2 • Gemini API Integration and Automation

Embeds Gemini API calls into scripts, bots, and internal tools for automated SDLC tasks. Covers authentication, rate limiting, and error handling patterns.

Lesson 3 • Scaling Gemini Across Large Teams

Addresses governance, access control, and standardization challenges in enterprise-scale AI adoption. Produces a rollout plan for multi-team SDLC integration.

Lesson 4 • AI Governance and Output Validation

Establishes review gates, human-in-the-loop checkpoints, and audit trails for AI-generated artifacts. Ensures compliance with organizational quality standards.