+ 1. Introduction to Prompt Engineering:
Definition of Prompt Engineering, Definition of Generative AI,
Importance of Prompt Engineering in Generative AI.
+ 2. Understanding Large Language Models (LLMs):
Overview of LLMs, Popular LLMs (GPT-3, GPT-4, Claude, Gemini, LlaMa, Copilot),
Open-source LLMs (GPT-J, LlaMa, FLAN-T5, BERT, CodeGen, Phi and more), etc.
+ 3. Understanding Prompt Engineering Strategies:
Instruction-Based Prompting, Context-Based Prompting, Example-Based Prompting,
Role-Based Prompting, etc.
+ 4. Prompt Usage Techniques:
Zero-shot Learning, One-shot Prompt, Few-shot Learning, Fine-tuning, etc.
+ 5. Understanding the Transformer Architecture:
Define Transformers Architecture: Introduction to Attention Mechanism,
Understand Encoder, Decoder and Encoder-decoder, Key Layers (7 Layers),
Understanding Query (Q), Key (K), and Value (V).
+ 6. Understanding Natural Language Processing (NLP):
Overview of NLP Concepts, Key Techniques and Tools, etc.
+ 7. Understanding Neural Networks in NLP / LLM:
Types of Neural Networks: Convolutional Neural Networks (CNN), Recurrent Neural Networks (RNN),
Long Short-Term Memory (LSTM), etc.
+ 8. Understand KEY LLM Parameters and its Settings:
Temperature, Top-K, Top-P, Presence Penalty, Frequency Penalty, Stop Sequences,
Set Max Tokens, etc.
+ 9. Common Challenges in LLMs:
Hallucination in LLM, Ambiguity in Prompt Design, Bias and Fairness, etc.
+ 10. Understanding Tokenization and Its Elements in LLM:
Tokenization Process in LLMs, Chunking, Context Window, etc.
+ 11. Advanced Prompt Engineering Models:
Retrieval-Augmented Generation (RAG), Chain-of-Thought (COT), ReAct (Reasoning and Acting),
Self-consistency, Tree-of-Thought Prompting (ToT), etc.
+ 12. Advanced concepts: Ethical Considerations in Prompting:
Understand Ethical Considerations in LLM, Prompting Security, Fairness & Bias, Accountability,
and Transparency, etc.
+ 13. Prompt Techniques and Strategies:
Examples of Effective Prompting, Real-world Use Cases and Best Practices, etc.
+ 14. Advanced Concepts of LLM Should Cover:
Computational Linguistics used in LLM Training and execution, Behind the LLM Pre-training Methods
and Computational linguistics, How LLMs are Trained in Quantum Machines, Generative Adversarial
Networks (GAN) in LLM, Variational AutoEncoders (VAE) in LLM, etc.
+ 15. Best Practice Tools: (Live):
Huggingface, Langchain framework, Google Vertex AI, Google AI Agent Builder (AI Agents), Create Custom
GPTs by ChatGPT, etc.
