Generative AI: Prompt Engineering Basics

Chapters

1Foundations of Generative AI

2LLM Behavior and Capabilities

3Core Principles of Prompt Engineering

4Writing Clear, Actionable Instructions

5Roles, Personas, and System Prompts

6Supplying Context and Grounding

7Examples: Zero-, One-, and Few-Shot

8Structuring Outputs and Formats

9Reasoning and Decomposition Techniques

10Iteration, Testing, and Prompt Debugging

11Evaluation, Metrics, and Quality Control

12Safety, Ethics, and Risk Mitigation

13Tools, Functions, and Agentic Workflows

14Retrieval-Augmented Generation (RAG)

RAG Concepts and Benefits Embeddings and Vectorization Indexing and Chunking Tactics Query Construction Prompts Re-Ranking and Fusion Citation and Attribution Formats Freshness and Recency Strategies Reducing Hallucinations with RAG Hybrid Sparse–Dense Search Context Compression Techniques Budget-Aware Retrieval RAG Evaluation Methods Answer–Source Separation Dynamic Routing and Switching Vector Store Hygiene

15Multimodal and Advanced Prompt Patterns

Courses/Generative AI: Prompt Engineering Basics/Retrieval-Augmented Generation (RAG)

Retrieval-Augmented Generation (RAG)

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Combine prompts with retrieval to ground answers in external knowledge, improving accuracy and traceability.

Content

13 of 15

Answer–Source Separation

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