Artificial Intelligence, Automation, and Machine Learning Peru

Generative AI for Software Development and Engineering Teams Training Course

Software teams are already using generative AI to draft code, explain legacy logic, generate tests, and accelerate refactoring, yet many organizations still struggle to turn isolated prompt use into repeatable engineering practice. The gap shows up quickly in inconsistent code quality, weak review discipline, hidden security risks, and uneven team adoption, especially as AI-assisted development tools and cloud-native delivery workflows change how engineering work gets done.

Generative AI for software development and engineering teams is a practical discipline for using large language models, coding assistants, and structured prompting to support design, implementation, testing, documentation, and review. It enables professionals to produce higher-quality code faster, reduce repetitive engineering effort, and apply AI responsibly across the software development lifecycle. This course is designed for software engineers, senior developers, DevOps engineers, QA automation specialists, engineering managers, and technical leads who need a credible, hands-on way to adopt generative AI in day-to-day delivery. You will work with prompt patterns, code-generation workflows, test-generation approaches, and review guardrails to produce reusable prompts, AI-assisted coding checklists, test strategies, and team adoption plans that improve how software teams build and ship software.

Duration
5 Days
Duration
Certificate
Certificate
Included
Delivery
Instructor-Led
Delivery
Level
Intermediate
Level
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Live Online Training

Join from anywhere with interactive virtual sessions

Starts
Ends
Weekend (4 Wks)
USD 850
Starts
Ends
Mon - Fri (5 Days)
USD 850
Starts
Ends
Weekend (4 Wks)
USD 850
Starts
Ends
Mon - Fri (5 Days)
USD 850
Starts
Ends
Weekend (4 Wks)
USD 850
Starts
Ends
Mon - Fri (5 Days)
USD 850
Starts
Ends
Mon - Fri (5 Days)
USD 850

Classroom Training

In-person sessions at premier locations

Nairobi Kenya
Mon - Fri
5 Days
USD 1,600
Kigali Rwanda
Mon - Fri
5 Days
USD 1,900
Dubai United Arab Emirates (UAE)
Mon - Fri
5 Days
USD 4,100
Addis Ababa Ethiopia
Mon - Fri
5 Days
USD 2,400
Customized Content
Team Training
Flexible Dates

In-person training at our premier venues — pick a city and date that works for you.

Location Duration Fee Language
Nairobi, Kenya Mon - Fri (5 Days) USD 1,600 English See dates & reserve →
Kigali, Rwanda Mon - Fri (5 Days) USD 1,900 English See dates & reserve →
Dubai, United Arab Emirates (UAE) Mon - Fri (5 Days) USD 4,100 English See dates & reserve →
Addis Ababa, Ethiopia Mon - Fri (5 Days) USD 2,400 English See dates & reserve →
Abuja, Nigeria Mon - Fri (5 Days) USD 2,800 English See dates & reserve →
Zanzibar, Tanzania Mon - Fri (5 Days) USD 2,400 English See dates & reserve →
Mombasa, Kenya Mon - Fri (5 Days) USD 1,700 English See dates & reserve →
Cape Town, South Africa Mon - Fri (5 Days) USD 3,900 English See dates & reserve →
Johannesburg, South Africa Mon - Fri (5 Days) USD 3,500 English See dates & reserve →
Pretoria, South Africa Mon - Fri (5 Days) USD 3,300 English See dates & reserve →
Kampala, Uganda Mon - Fri (5 Days) USD 1,900 English See dates & reserve →
Lagos, Nigeria Mon - Fri (5 Days) USD 2,500 English See dates & reserve →
Arusha, Tanzania Mon - Fri (5 Days) USD 2,000 English See dates & reserve →
Dar es Salaam, Tanzania Mon - Fri (5 Days) USD 1,900 English See dates & reserve →
Accra, Ghana Mon - Fri (5 Days) USD 3,800 English See dates & reserve →
Naivasha, Kenya Mon - Fri (5 Days) USD 1,700 English See dates & reserve →

Live, instructor-led sessions you can join from anywhere — pick the next start date below.

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About the Course

Software leaders increasingly want results they can prove in engineering practice, not just experiments with chatbots. In this domain, you need to demonstrate AI-assisted coding fluency, prompt discipline, code review judgment, test generation skill, secure use of model outputs, and release governance aligned with established engineering controls such as ISO/IEC/IEEE 12207 and OWASP guidance. Generative AI for software development and engineering teams gives you a structured way to move from ad hoc prompting to repeatable use cases that support delivery quality and speed.

The course turns scattered experimentation into a working system for software delivery. You will practice prompt design for code generation, test case creation, debugging support, refactoring suggestions, documentation drafting, and engineering knowledge capture. You will also be introduced to model-risk controls, code review guardrails, and team workflows that support tools such as ChatGPT, GitHub Copilot, and similar coding assistants, while keeping implementation honest about what AI can and cannot do in a real development team. What you will learn: you will learn how to use generative AI to accelerate coding, testing, and documentation while keeping review, security, and quality controls in place. You will practice building prompts, reviewing AI-generated code, and drafting team guidance; you will be introduced to governance patterns for responsible adoption at team level.

The course is built for teams that face delivery pressure, legacy code complexity, uneven AI literacy, and constant scrutiny over software quality. It is especially relevant where engineering groups must collaborate across product, security, QA, and operations while adopting AI tools without creating unacceptable risk. The approach assumes realistic constraints such as limited time for experimentation, mixed tool maturity, and the need to prove value quickly through tangible outputs rather than abstract innovation claims. This course teaches generative AI for software development through applied labs so you can ship practical workflows instead of isolated demos.


Target Audience

This course is designed for engineering professionals who need practical, team-ready ways to use generative AI in software delivery, testing, and code governance.

  • Software Engineers who want to draft, refactor, and review AI-assisted code safely.
  • Senior Developers who need to standardize prompt patterns across a feature team.
  • QA Automation Engineers who generate test cases and edge-condition checks with LLMs.
  • DevOps Engineers who apply generative AI to release notes and deployment support.
  • Engineering Managers who need repeatable AI use cases for delivery teams.
  • Technical Leads responsible for code quality, review discipline, and team adoption.
  • Product Engineers who bridge product requirements into faster implementation workflows.
  • Platform Engineers who document internal tooling and reusable developer guidance.
  • Security Engineers who evaluate AI-assisted coding risks and prompt leakage.
  • Software Architecture Leads who assess where generative AI fits in design and review.

Course Objectives

This course equips you to plan, execute, and measure generative AI for software development initiatives that accelerate delivery, strengthen engineering controls, and support responsible team adoption.

  • Assess your current software delivery workflow using a Generative AI readiness checklist and code review map.
  • Apply prompt engineering techniques to generate code, tests, and documentation in realistic engineering tasks.
  • Design reusable prompt templates for debugging, refactoring, and API integration support.
  • Build an AI-assisted test generation workflow for unit and edge-case coverage.
  • Calculate quality impacts using defect density, test coverage, and review rework metrics.
  • Evaluate AI-generated code against OWASP guidance, secure coding rules, and team standards.
  • Implement usage guardrails for Copilot- or ChatGPT-supported development in a team setting.
  • Synthesize adoption findings into an engineering playbook, rollout plan, and stakeholder update.

Requirements & Prerequisites

Intermediate familiarity with software development concepts is required, including source control, debugging, code review, and basic testing practices. You should have working knowledge of at least one programming language used in your team, but you do not need advanced machine learning experience. Coding/programming is not required for course completion beyond reading, editing, and evaluating sample code, although hands-on lab work will ask you to use prompts and review AI-generated outputs. Advanced concepts such as model governance and secure usage patterns are taught at the operational application level, not at production engineering depth. A laptop with browser access is recommended.


Professional and Organizational Impact

When you lead generative AI for software development with credible data and practical strategies, you become a trusted driver of delivery speed and engineering quality.

  • Build confidence reviewing AI-generated code and test output.
  • Gain practical prompting skill for coding, debugging, and refactoring.
  • Strengthen judgment around secure code and hallucinated suggestions.
  • Enhance your ability to balance speed with review discipline.
  • Develop reusable prompt libraries for day-to-day engineering work.
  • Position yourself as a credible AI adoption guide for your team.
  • Expand your influence across product, QA, DevOps, and security partners.
  • Support your career growth in AI-enabled software engineering roles.

Organizations that embed generative AI for software development into engineering workflows reduce costs, mitigate risks, and build lasting competitive advantage.

  • Reduce developer time spent on repetitive drafting and boilerplate coding.
  • Improve test coverage through faster AI-assisted test creation.
  • Lower review rework by standardizing prompt and review guardrails.
  • Mitigate security risk from unchecked AI-generated code suggestions.
  • Shorten cycle time for documentation, debugging, and refactoring tasks.
  • Strengthen engineering consistency across distributed or hybrid teams.
  • Improve delivery visibility through reusable AI usage and quality metrics.
  • Position the engineering function for faster AI-enabled product delivery.

Training Methodology

This is a practical, outcome-driven course designed to turn generative AI for software development aspiration into measurable action and credible reporting.

Methodology includes:

  • Hands-on prompt lab using a code-generation dataset and defect-fix task.
  • Scenario simulation for a production bug triage with AI-assisted debugging.
  • Assessment using an AI code review checklist and secure coding rubric.
  • Stakeholder mapping for engineering, QA, security, and product approval flow.
  • Case analysis from fintech, SaaS, healthcare software, and enterprise IT teams.
  • Group workshop producing a prompt library and AI coding guardrail draft.
  • Reflection exercise against benchmarked review time, defect rate, and test coverage data.

Upcoming Sessions

Next available dates worldwide

Virtual

(Zoom) Training
USD 850
29th Jun-3rd Jul 2026

Nairobi

Kenya
USD 1,600
6th Jul-10th Jul 2026

Kigali

Rwanda
USD 1,900
13th Jul-17th Jul 2026

Dubai

United Arab Emirates (UAE)
USD 4,100
22nd Jun-26th Jun 2026

Zanzibar

Tanzania
USD 2,400
22nd Jun-26th Jun 2026

Addis Ababa

Ethiopia
USD 2,500
29th Jun-3rd Jul 2026

Abuja

Nigeria
USD 2,800
6th Jul-10th Jul 2026

Mombasa

Kenya
USD 1,700
6th Jul-10th Jul 2026

Cape Town

South Africa
USD 3,900
15th Jun-19th Jun 2026

Johannesburg

South Africa
USD 3,500
20th Jul-24th Jul 2026

Kampala

Uganda
USD 1,900
15th Jun-19th Jun 2026

Pretoria

South Africa
USD 3,300
27th Jul-31st Jul 2026

Lagos

Nigeria
USD 2,500
15th Jun-19th Jun 2026

Certification

Recognized credentials that advance your career

Participants who complete the Generative AI for Software Development and Engineering Teams Training Program earn a Trainingcred Certificate of Achievement, demonstrating professional competence and alignment with global standards in learning and development.

NITA Accredited

Accredited by the National Industrial Training Authority, ensuring programs meet nationally recognized standards of quality and relevance.

CPD Certified

Recognized by the CPD Certification Service, ensuring every program meets internationally benchmarked standards of professional excellence.

Why this course earns its place on your CV

Accredited training, practitioner trainers, and peers on the same career track — the three things real expertise is built on.

Effective Learning & Skill Development

  • Build expertise with structured, outcome-driven learning.
  • Equip individuals and teams with skills that grow with industry needs.
  • Reinforce learning through real-world scenarios, case studies and practical exercises.

Career Growth & Professional Advancement

  • Apply what you learn with a proven methodology that ensures lasting impact.
  • Develop immediately usable skills that translate directly into workplace success.
  • Gain the expertise needed for career advancement and leadership roles.

Training Optimization & Learning Excellence

  • Tailor training to industry-specific challenges and organizational goals.
  • Use data-driven insights and automation to enhance training effectiveness.
  • Evaluate progress and ensure long-term learning success.

Industry Tools and Platforms Featured in this Training

The platforms and vendors Peru teams are running today — taught against real configurations, not generic vendor demos.

3
  • GitHub Copilot GitHub
    Used to draft code, suggest refactors, and speed up routine development tasks in IDE workflows.
  • ChatGPT OpenAI
    Used for prompt-driven code explanation, debugging support, and generating test ideas or documentation drafts.
  • Visual Studio Code Microsoft
    Used as the primary development environment where AI-assisted coding and review workflows can be applied inside the editor.

Real Results from Real Professionals

Thousands of professionals have transformed their careers through our training programs. Now, it's your turn.

PE Built for Peru

How this course applies where you work

Local laws, real case studies, and data-points that make the curriculum land — not generic global theory.

Business Results You Can Expect

How participants put this to work the week after training — and the measurable return their organisation can plan for.

How participants apply this

Participants use generative AI to accelerate routine coding work, explain unfamiliar or legacy code, and draft tests before manual review. In day-to-day team settings, they turn ad hoc prompting into repeatable practices for feature development, refactoring, debugging, and documentation. They also learn to use AI outputs as starting points rather than final code, so pull requests still pass through normal engineering review, testing, and security checks. For teams in Peru, the practical value is in making these workflows consistent across developers, not just dependent on a few early adopters.

Expected ROI

After training, teams usually see faster turnaround on repetitive development tasks and lower time spent on first-draft code, test scaffolding, and documentation. The main business gain is not replacement of engineers but better throughput from the same team, with more time available for design, defect reduction, and production hardening. Over 6–12 months, organizations typically benefit most when they standardize prompt patterns, code-review guardrails, and AI usage rules across the team. The strongest returns usually come from teams that already have disciplined CI/CD and can absorb AI assistance into existing delivery practices.

Frequently Asked Questions

Got questions? We've gathered the answers to common queries to help you feel confident and informed.

No. The practical goal is to use AI to speed up drafting, debugging, testing, and documentation while keeping engineers responsible for architecture, correctness, security, and review.

Software engineers, senior developers, DevOps engineers, QA automation specialists, engineering managers, and technical leads benefit most because they can apply AI across implementation, testing, and review workflows.

Generative AI can help summarize older code, explain dependencies, suggest refactoring steps, and produce regression tests before changes are merged. That makes it easier for teams to modernize codebases without relying entirely on tribal knowledge.

Teams should keep normal code review, automated tests, secret scanning, and security review in place. AI outputs should be treated as drafts that need verification, especially for correctness and licensing-sensitive code.

Trusted by 100+ organizations across 40+ countries

Premier Bank
Amnesty International
UNDT SACCO
UNFPA
USAID
AMREF Health Africa
KENTRADE
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UNDP
GIZ
Premier Bank
Amnesty International
UNDT SACCO
UNFPA
USAID
AMREF Health Africa
KENTRADE
CPF
UFIA
UNICEF
Central Bank of Kenya
UNDP
GIZ
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Bank of Rwanda
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Dorcas Aid
Finn Church Aid
KCB Foundation
Ministry of Education Saudi Arabia
NSSF Uganda
RBA
Reserve Bank of Malawi
WASREB Kenya
Virginia Commonwealth University
Barbours
Bank of Rwanda
RFA
Dahabshil Bank
Dorcas Aid
Finn Church Aid
KCB Foundation
Ministry of Education Saudi Arabia
NSSF Uganda
RBA
Reserve Bank of Malawi
WASREB Kenya
Virginia Commonwealth University