Generative AI & Prompt Engineering Masterclass
Master LLM prompting techniques, In-Context Learning, Chain-of-Thought, and RAG architectures.
The assessment unlocks once you have worked through the lessons.
Independent provider. Not an accredited awarding body. Not a regulated qualification.
What You Will Master
- Master zero-shot, few-shot, and multi-turn persona prompt patterns
- Implement step-by-step Chain-of-Thought (CoT) and Self-Consistency prompting
- Design unbreakable System Prompts and mitigate prompt injection vulnerabilities
- Format and validate deterministic JSON and schema-based outputs from LLMs
- Understand Retrieval-Augmented Generation (RAG) chunking, embedding & vector retrieval
Skills Acquired
Prerequisites
- Basic understanding of how to interact with conversational AI
- Fundamental interest in software development or AI product workflows
Curriculum Syllabus
4 modules · 6 lessons · 20 min · one assessment
Module 1: Foundations of LLMs & Tokenization
Understand how Large Language Models predict next tokens, context window management, and temperature dynamics.
Module 2: Advanced In-Context Prompting Techniques
Master Few-Shot Prompting, Chain-of-Thought (CoT), Step-Back Prompting, and Self-Consistency.
Module 3: Structured Outputs, JSON Schemas & Guardrails
Force deterministic schema adherence, regex matching, and bulletproof output formatting for API pipelines.
Module 4: Retrieval-Augmented Generation (RAG) Architecture
Connecting LLMs to proprietary knowledge bases, vector search, chunking strategies, and hybrid retrieval.
Md. Rajib Hawlader
Principal AI Research Scientist, NeuroLearn AI Labs
Writes and maintains the NeuroLearn AI curriculum.
Ready to Begin Generative AI & Prompt Engineering Masterclass?
Self-paced curriculum with free instant certificate issuance upon passing the evaluation.
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