Learn Practical Generative AI for Modern Applications
Understand how generative AI systems are used in real workflows. Learn prompt design, model interaction, responsible usage and application-integration concepts for productivity, software and content-oriented use cases.
LLM Foundations
Understand how large language model applications are structured at a practical level.
Prompt Design
Learn systematic prompting, context design and output evaluation.
Application Workflows
Explore API and automation-oriented GenAI integration concepts.
Responsible Use
Understand privacy, hallucination, evaluation and safe-use considerations.
Who This Course Is For
- Developers exploring AI-enabled applications.
- Students learning modern AI workflows.
- Professionals using AI for productivity and automation.
- Data/AI learners moving into generative systems.
What You Will Be Able to Do
- Design better prompts and structured AI interactions.
- Evaluate outputs for quality and reliability.
- Understand embeddings/RAG concepts at a foundation level.
- Prototype simple AI-enabled workflows.
- Use GenAI responsibly in practical projects.
Course Curriculum
Generative AI Foundations
- Generative AI concepts
- LLM application basics
- Tokens/context concepts
- Prompt anatomy
- Limitations and hallucinations
Prompt Engineering
- Instruction design
- Few-shot examples
- Structured output
- Context management
- Prompt evaluation
- Reusable prompt patterns
Application Concepts
- API integration foundations
- Embeddings concepts
- RAG foundations
- Tool use / function calling concepts
- Workflow automation patterns
Responsible GenAI
- Privacy
- Bias
- Output verification
- Copyright awareness
- Security considerations
- Evaluation and monitoring concepts
Projects & Practical Work
Project difficulty is adjusted to the learner's level and batch progress. The goal is to turn concepts into demonstrable work rather than only complete theory modules.
- Prompt library project
- AI assistant prototype
- RAG-style concept project
- GenAI workflow capstone
Career Preparation
Mango Engineers' learning process can include project review, portfolio/GitHub readiness, resume guidance, mock interview practice and placement assistance. Placement assistance is support, not a job guarantee.
- AI Application Developer pathway
- GenAI workflow/automation pathway
- Software Developer with AI skills
- AI product learning pathway
Frequently Asked Questions
Basic coding is helpful for application integration. Non-coders can still learn prompting and workflow concepts.
Yes. Prompt design, structured outputs and evaluation are core parts of the pathway.
RAG and embeddings are introduced at a foundation level, with practical depth depending on the batch.
No. Career assistance can be provided, but employment is not guaranteed.
