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AI theory, architecture, models
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Module 17
LLM Interaction: Prompting and In-Context Learning
1
Zero-shot and Few-shot Prompting
Apply zero-shot and few-shot prompting for in-context learning
2
Chain-of-Thought Prompting for Enhanced Reasoning
Implement Chain-of-Thought (CoT) prompting to improve reasoning
3
Self-Consistency for Robust CoT Reasoning
Apply self-consistency to enhance the robustness of CoT reasoning
4
Prompt Engineering for Task Optimization
Engineer effective prompts for specific task optimization
5
Advanced Prompting for Complex Problem-Solving
Apply advanced prompting strategies like Tree of Thoughts for complex problem-solving
Previous module
Aligning and Fine-Tuning Large Language Models
Next module
Retrieval-Augmented Generation (RAG)