This course is suitable for individuals who are new to prompt engineering and want to gain a comprehensive understanding of the field. Throughout the course, participants will explore key concepts such as prompt design principles, user interaction patterns, and the psychology behind prompt effectiveness. They will learn how to analyze user needs, design prompts that align with user expectations, and ensure a seamless and intuitive user experience.
The curriculum covers various topics, including prompt types, prompt scripting languages, error handling, and user feedback integration. Participants will also gain hands-on experience through practical exercises and real-world case studies, allowing them to apply their knowledge in practical scenarios.
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Introduction to Conversational AI: Overview and Applications
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Multi-turn dialogue modeling
- Sequence-to-sequence models for dialogue generation
- Sequence-to-sequence models for dialogue generation (Quiz 1)
- Multi-modal conversational agents (text, speech, visuals)
- Multi-modal conversational agents (text, speech, visuals) (Quiz 1)
- Explainable and interpretable conversational agents
- Explainable and interpretable conversational agents (Quiz 1)
- Multilingual and cross-cultural Conversational AI
- Multilingual and cross-cultural Conversational AI (Quiz 1)
- Context-awareness and memory in dialogue systems
- Context-awareness and memory in dialogue systems (Quiz 1)
- Emotion detection and sentiment analysis in conversations
- Emotion detection and sentiment analysis in conversations (Quiz 1)
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Machine Learning
- Machine Learning Approaches for Conversational AI
- Machine Learning Approaches for Conversational AI (Quiz 1)
- Supervised learning for intent classification
- Supervised learning for intent classification (Quiz 1)
- Reinforcement learning in dialogue systems
- Reinforcement learning in dialogue systems (Quiz 1)
- Generative Models
- Generative Models (Quiz 1)
- Transfer learning and pre-trained models for Conversational AI
- Transfer learning and pre-trained models for Conversational AI (Quiz 1)
- Transformer models
- Transformer models (Quiz 1)
- Evaluation and Metrics in Conversational AI
- Evaluation and Metrics in Conversational AI (Quiz 1)
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Natural Language Processing (NLP) Fundamentals
- Introduction to NLP and its role in Conversational AI
- Introduction to NLP and its role in Conversational AI (Quiz 1)
- Language modeling and understanding (introduction)
- Language modeling and understanding (Quiz 1)
- Segmentation
- Segmentation (Quiz 1)
- Tokenizing
- Removing Stop Words
- Tokenizing. Removing Stop Words (Quiz 1)
- Stemming
- Lemmatization
- Stemming. Lemmatization (Quiz 1)
- Part of Speech Tagging
- Named Entity Tagging
- Part of Speech Tagging. Named Entity Tagging (Quiz 1)
- Natural Language Understanding (NLU) and Natural Language Generation (NLG)
- Natural Language Understanding (NLU) and Natural Language Generation (NLG) (Quiz 1)
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Introduction to Prompt Engineering
- Basics of Prompting
- Basic Prompts
- Prompt Formatting
- Elements of a Prompt
- Start Simple
- Avoid Impreciseness
- Examples of Prompts
- Text Summarization
- Information Extraction
- Question Answering
- Text Classification
- Conversation
- Code Generation
- Reasoning
- Prompting Techniques
- Few-Shot Prompting
- Limitations of Few-shot Prompting
- Chain-of-Thought (CoT) Prompting
- Zero-shot COT Prompting
- Self-Consistency
- Generated Knowledge Prompting
- Automatic Prompt Engineer (APE)
- Active-Prompt
- Directional Stimulus Prompting
- Introduction to ReAct Prompting
- ReAct Prompting
- Results on Knowledge-Intensive Tasks
- Results on Decision Making Tasks
- Multimodal CoT Prompting
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Introduction to Programming
- Introduction
- Python objects, basic types, and variables
- Basic operators
- Basic containers
- Accessing data in containers
- Python built-in functions and callables
- Python object attributes (Some methods on string objects)
- Python object attributes (Some methods on list objects)
- Python object attributes (Some methods on set objects)
- Python object attributes (Some methods on dict objects)
- Functions. Positional arguments and keyword arguments to callables
- Formatting strings and using placeholders
- Python “for loops”
- Python “if statements”
- Python “while loops”
- Classes: Creating your own objects