Course overview
Moving from AI experiments to real applications requires solid engineering. Learn how to integrate AI models, ground them in your own data and deploy features people can rely on.
About this course
Professional AI App Development is an advanced applied course for learners who want to build real AI-powered applications. You will work with AI APIs and model integrations, build AI-powered application features, learn the fundamentals of retrieval-augmented generation (RAG) and connect applications to external knowledge sources. The course concludes with testing, evaluation and reliability, followed by deployment and practical production considerations.
Who this course is for
- Developers who want to add AI capabilities to their applications
- Technical founders building AI-powered products
- Technically confident professionals moving from no-code to code
Skill level and prerequisites
| Skill level | Advanced |
|---|---|
| Prerequisites | Basic programming experience (for example in Python or JavaScript) and familiarity with using APIs. Model provider API usage and hosting services are billed separately by those providers and are not included. |
| Format | Self-paced online course |
| Language | English |
| Access | Course access or download link sent by email within 5 minutes of purchase |
What you will learn
By the end of this course, you will be able to:
- Integrate AI models into applications through their APIs
- Build AI-powered application features with sound engineering practices
- Explain and implement the fundamentals of retrieval-augmented generation (RAG)
- Connect applications to external knowledge sources
- Test, evaluate and deploy AI features with production considerations in mind
Complete curriculum
- Module 1: Working with AI APIs and model integrationsAuthentication, requests, parameters, structured outputs and cost awareness.
- Module 2: Building AI-powered application featuresDesigning features, handling responses and creating a good user experience.
- Module 3: Retrieval-augmented generation (RAG) fundamentalsEmbeddings, chunking, retrieval and grounded generation.
- Module 4: Connecting applications to external knowledge sourcesDocuments, databases and services as reliable sources of context.
- Module 5: Testing, evaluation and reliabilityTest sets, evaluation methods, error handling and monitoring.
- Module 6: Deployment and practical production considerationsDeployment options, security, privacy, performance and cost management.
What is included
- 6 structured modules covering the complete curriculum above
- Practical exercises at the end of every module
- Starter code examples, an architecture reference and a deployment checklist
- A guided final project: Build an AI-powered application that integrates a model API and a RAG knowledge source, evaluate it and deploy it.
- Self-paced online access, delivered to your inbox within 5 minutes
Key benefits
- Build real applications. Go beyond prototypes and build AI features designed for actual users.
- Grounded, accurate answers. Use RAG to connect AI to your own knowledge and reduce unsupported responses.
- Production-ready thinking. Learn the testing, security and deployment practices that real products require.
Practical outcome
Build and deploy an AI-powered application project.
Course access and delivery
This is a 100% digital product. Nothing will be shipped. As soon as your payment is confirmed, your course access or download link is sent to the email address you provide at checkout, normally within 5 minutes. If it has not arrived, please check your spam or promotions folder, then contact contact@skalioacademy.com with your order number.
7-day money-back guarantee
Enroll with confidence. If the course is not right for you, email us within 7 days of purchase with your order number and we will refund the full purchase price to your original payment method. Course access is deactivated once the refund is processed. See our Refund Policy for full details.