Mastering Prompt Engineering

1: Basic Insights into Large Language Models

1.1 The Rise of Large Language Models (LLMs) and Generative AI

1.2 Importance of Prompt Engineering for Enhancing LLMs

1.3 History and Background of LLM


2: Foundations of LLM-based Prompt Engineering

2.1 Understanding LLMs: Architecture, Training, and Fine-tuning

2.2 Introduction to Prompt Engineering and its Role in LLMs

2.3 Why Prompt Engineering and its Working?

2.4 Types of Prompts: Conceptual, Contextual, and Conditioning

2.5 Elements of Prompt

2.6 Evaluating Prompt Effectiveness and Quality


3: Familiarity with Prompt Design

3.1 Components of Prompt

3.2 Types of Prompts: Single-Sentence, Multi-Sentence, Query-Based, etc.

3.3 Formatting Guidelines for Effective Prompts

3.4 Selecting Appropriate Prompt Lengths and Granularity

3.5 Handling Special Characters and Symbols in Prompts


4: Pre-processing and Tokenization in Prompt Engineering

4.1 Foundation Concept of Tokenization

4.2 Tokenization Techniques for Different Prompt Types

4.3 Special Tokens and Their Usage in Prompts

4.4 Handling Input Formatting Variations in Prompts

4.5 Multilingual Prompts: Considerations and Techniques


5: State-of-the-Art Techniques in Prompt Engineering

5.1 Cost-effective Techniques for SMEs

5.2 Accessibility: User-friendly Frameworks and Tools

5.3 Community Efforts: Open-source Initiatives and Libraries

5.4 Rule-based Approaches for Prompt Design and Modification

5.5 Template-based Prompts and Language Patterns

5.6 Reinforcement Learning for Automatic Prompt Optimization

5.7 Knowledge Graph Integration for Contextual Prompts

5.8 GAN-based Approaches for Conditioning Prompts

5.9 Comparative Analysis of Prompt Engineering Techniques


6: Diverse Prompt Engineering Models and their Implementations

6.1 Types of Models: BLOOM, GPT 3.5, GPT4, LLAMA, PALM2, LANGCHAIN

6.2 Comparison of Large Language Models

6.3 Advanced Techniques in Prompt Engineering

6.4 Implementing Prompt Engineering

6.5 SMEs and Specific Use Cases

6.6 Ease of Use: Beginner-friendly Interfaces and Tutorials

6.7 Budget Considerations: Cost-effective Models and Free Tier Options


7: Evaluation and Refinement of Prompt Engineering

7.1 Metrics for Evaluating Prompted Generation Quality

7.2 Human Evaluation Methods and User Studies

7.3 Iterative Refinement and Improvising Prompt Quality


8: Prompt Engineering: Ethical Considerations and Challenges

8.1 Introduction to Ethical Considerations in Generative AI

8.2 Bias and Fairness 8.3 Privacy and Security Concerns

8.4 Transparency and Responsibility

8.5 Accountability and Explainability

8.6 SME-specific Risks

8.7 Practical Best Practices 8.8 Transparency and Explainability Tools


9: Case Studies in Prompt Engineering

9.1 Case Study 1: Building and Fine-tuning a Domain-Specific LLM with Prompts

9.2 Case Study 2: Cross-lingual Transfer Learning with Multilingual LLMs and Prompts

9.3 Case Study 3: Controlled Text Generation with Conditional LLMs and Prompts

9.4 Case Study 4: PALM2: Adaptively Large Models for Efficient Training and Inference with Prompts

9.5 Case Study 5: LangChain: Contextual Language Models with External Knowledge and Prompts

9.6 SME Success Stories

9.7 Quantifiable Results 9.8 Challenges and Solutions


10: Future Trends in Large Language Models and Prompt Engineering cum Concluding Remarks

10.1 Advances in LLM Architectures and Training Techniques

10.2 Augmented Prompt Engineering: Human and AI Collaboration

10.3 Explainability and Interpretability in LLM-based Prompt Engineering

10.4 Democratization of Access

10.5 Interdisciplinary Collaborations

10.6 Human-AI Co-creation

10.7 Conclusion and Future Scope
Glossary
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