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Technology
Generative AI + Prompt Engineering
Generative AI + Prompt Engineering
Curriculum
11 Sections
30 Lessons
17 Weeks
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Module 1: Introduction to Generative AI
3
1.1
Overview of generative AI
1.2
History and evolution
1.3
Applications and impact
Module 2: Large Language Models (LLMs)
2
2.1
Understanding LLMs like GPT-3 and GPT-4
2.3
Applications of LLMs in various domains
Module 3: Natural Language Processing (NLP)
3
4.1
Basics of NLP
4.2
Text generation and summarization
4.3
Sentiment analysis and language translation
Module 4: Different LLMs
3
5.1
Gemini, Llama
5.2
Tokens, vectors, and embeddings
5.3
Vector data bases
Module 5: Retrieval Augmented Generation (RAG)
4
6.1
RAG components
6.2
Chunking, vector DB, semantic search.
6.3
Retrieval, filtering
6.4
Generation
Module 6: Fine Tuning LLMS
2
7.1
Training and fine-tuning LLMs
7.2
Fine tuning with various techniques
Module 7: Prompt Engineering
3
8.1
Crafting effective prompts
8.2
Techniques for improving prompt performance
8.3
Case studies and practical examples
Module 8: Ethical Consideration
3
9.1
Ethical implications of generative AI
9.2
Bias and fairness in AI models
9.3
Responsible AI practices
Module 9: Hands-On Applications
2
10.1
Use generative AI tools
10.2
Real-world case studies
Module 10: Data Wrangling and Preprocessing
3
11.1
Techniques for preparing data for AI models
11.2
Data cleaning and transformation
11.3
Handling large datasets
Module 11: Advanced Topics
2
12.1
Exploring cutting-edge research in generative AI
12.2
Future trends and developments
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