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Overview
This immersive three-day course introduces software developers to the transformative role of artificial intelligence in modern development workflows. Participants will explore the landscape of AI-assisted coding tools, including GitHub Copilot, Amazon Q, and Tabnine, while learning prompt engineering strategies and security implications. Through hands-on labs, attendees will build an intelligent application using foundation models, deploy machine learning models locally and in the cloud, and develop red team techniques to understand and defend against common AI attacks. The course equips learners with future-ready skills essential for enhancing productivity, building smarter applications, and responsibly adopting AI in development environments.
Prerequisites
Participants should have:
- A solid understanding of software development principles and practices
- Experience using integrated development environments (IDEs) and version control systems
- Familiarity with cloud environments (Azure, AWS, or GCP) is beneficial
Target audience
This course is ideal for:
- Professional software developers and technical leads exploring AI integration
- DevOps and security engineers seeking to understand AI risks and defences
- Development teams aiming to increase productivity and build intelligent applications
- Organisations adopting AI-driven workflows and tools
Delegates will learn how to
By the end of this course, learners will be able to:
- Understand key AI concepts and their relevance to software development
- Employ prompt engineering techniques to interact effectively with language models
- Use AI-assisted development tools to streamline and accelerate coding workflows
- Build and enhance applications with AI-driven features and services
- Train and deploy machine learning models both locally and in cloud environments
- Identify and mitigate common AI attack vectors including prompt injection and model jailbreaks
- Apply AI securely in production-ready software development processes
Outline
Day 1 – AI foundations and productivity tools
AI in software development
- Industry impact and emerging trends
- Enhancing productivity with AI
- Overview of AI-assisted development workflows
The AI landscape
- GitHub Copilot: features, use cases, and demos
- Amazon Q Developer: generating and securing code
- Tabnine: privacy, team collaboration, and coding efficiency
- Cloud-based lab setup and introductory exercises
Prompt engineering fundamentals
- Core techniques and strategies
- SudoLang for structured prompting
- Handling limitations and hallucinations in AI models
- Guided translation of natural language to structured prompts
Day 2 – Building intelligent applications
Advanced coding with Copilot
- Autocomplete, unit testing, boilerplate generation
- Integrated prompt engineering and Copilot Chat
- Real-world exercises in unfamiliar environments and frameworks
AI tool comparison and best practices
- ChatGPT in IDEs
- Feature and performance comparisons
- Choosing the right AI assistant
Application development with AI integration
- Fresh Cart project overview
- Integrating LLM-based chatbots
- Prompt routing and orchestration
- Semantic search and state mutation
- Logging and self-correction for AI operations
Security in AI systems
- Prompt injection: risks and red team exercises
- Model jailbreaks and defences
- Prompt extraction techniques
- Defensive strategies using ReBuff, Llama Guard, and Lakera
Day 3 – Working with models and deployment
Foundation models and inference
- Open-source models: Llama 3, Stable Diffusion
- Running models locally and in containers
- Hugging Face: transformers, pipelines, AutoTrain, and inference endpoints
Cloud-based AI deployments
- Microsoft Azure: OpenAI Studio and hosting
- Google Cloud: Vertex AI and Model Garden
- Amazon AI: Bedrock and machine learning toolkits
- Replicate and Cog for containerised model deployment
Machine learning essentials
- TensorFlow and Keras workflows
- Model training, tuning, and deployment
- Serving models using Docker
Advanced AI concepts
- Fine-tuning models in OpenAI, Vertex AI, and AWS
- Embedding techniques and similarity search
- Retrieval Augmented Generation (RAG) applications
- Research tools and embedding flows in Fresh Cart
Exams and assessments
There are no formal exams in this course. Learning is reinforced through practical exercises, red team simulations, and guided projects. Interactive labs provide a hands-on approach to mastering AI tools and methodologies. Follow-on courses we recommend Certified AI Security Engineer.
Hands-on learning
This course includes:
- Interactive labs using Copilot, Hugging Face, and cloud services
- Guided exercises for secure AI application development
- Red team scenarios simulating real-world AI threats
- End-to-end development of an AI-enhanced application
- Continuous instructor support throughout labs and activities
Why choose QA
- Award-winning training, top NPS scores
- Over 500,000 learners in 2024
- Our training experts are industry leaders
- Read more about QA
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Frequently asked questions
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Find more answers to frequently asked questions in our FAQs: Bookings & Cancellations page.
How do QA’s virtual classroom courses work?
Our virtual classroom courses allow you to access award-winning classroom training, without leaving your home or office. Our learning professionals are specially trained on how to interact with remote attendees and our remote labs ensure all participants can take part in hands-on exercises wherever they are.
We use the WebEx video conferencing platform by Cisco. Before you book, check that you meet the WebEx system requirements and run a test meeting to ensure the software is compatible with your firewall settings. If it doesn’t work, try adjusting your settings or contact your IT department about permitting the website.
How do QA’s online courses work?
QA online courses, also commonly known as distance learning courses or elearning courses, take the form of interactive software designed for individual learning, but you will also have access to full support from our subject-matter experts for the duration of your course. When you book a QA online learning course you will receive immediate access to it through our e-learning platform and you can start to learn straight away, from any compatible device. Access to the online learning platform is valid for one year from the booking date.
All courses are built around case studies and presented in an engaging format, which includes storytelling elements, video, audio and humour. Every case study is supported by sample documents and a collection of Knowledge Nuggets that provide more in-depth detail on the wider processes.
When will I receive my joining instructions?
Joining instructions for QA courses are sent two weeks prior to the course start date, or immediately if the booking is confirmed within this timeframe. For course bookings made via QA but delivered by a third-party supplier, joining instructions are sent to attendees prior to the training course, but timescales vary depending on each supplier’s terms. Read more FAQs.
When will I receive my certificate?
Certificates of Achievement are issued at the end the course, either as a hard copy or via email. Read more here.