[100% Off] Generative Ai Engineer Masterclass
Build AI apps with Python, LLMs, prompt engineering, RAG, tools, and multimodal features through hands-on labs
What you’ll learn
- Build Python AI applications with model API calls
- clear prompts
- and validated structured outputs.,Create document question-answering assistants using embeddings
- vector search
- RAG
- and source citations.,Integrate validated calculator tools
- bounded agent workflows
- and image-description features into an AI assistant.,Evaluate AI responses
- prepare fine-tuning datasets
- and configure a web interface for deployment.
Requirements
- No prior AI experience required. Bring a computer
- basic computer skills
- and a willingness to learn Python. Paid API access is optional.
Description
Build a practical foundation in generative AI engineering by creating Python applications that combine language models, document retrieval, tools, and image inputs.
The Generative AI Engineer Masterclass is designed for beginners, students, aspiring AI engineers, and developers who want hands-on experience building AI applications. No previous AI experience is required. The course begins with development setup and Python essentials before introducing the components of a generative AI application.
You will learn how to write clear prompts, guide responses with examples, and validate structured outputs. You will explore document chunking, embeddings, vector storage, and retrieval-augmented generation, then build a document question-answering assistant that provides source references and handles missing evidence.
As you progress, you will add a calculator tool, validate its arguments, and understand how bounded agent workflows control execution. You will also prepare image inputs, explore image-description workflows, and learn to distinguish visible observations from uncertain interpretations.
The course covers how to choose between prompting, RAG, and fine-tuning. You will practice preparing consistent training examples, separating datasets, and checking for duplicate prompts without submitting a paid training job.
Through the course’s practical exercises, you will:
Build a simple chatbot and structured information extractor.
Retrieve relevant passages from course documents.
Generate answers supported by available evidence.
Validate calculator operations and image inputs.
Evaluate answers and investigate failures.
Connect features into a final assistant project.
Explore web interfaces, environment configuration, and deployment considerations.
Learning resources include runnable Python reference code, guided labs, quizzes, worksheets, and an HTML hands-on workbook covering all ten sections.
Core exercises run offline without paid API access. These examples use scripted chatbot responses, lexical retrieval, and recorded image fixtures to make application behavior easy to inspect. Optional live exercises introduce model-powered workflows and may incur API charges.
By the end of the course, you will be able to explain, build, test, and extend the core workflows of a beginner generative AI assistant.
