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Elite ML Engineer from Day 1
·
Module 6
Prompt Engineering Fundamentals
1
Zero-Shot Prompt Design for Classification, Extraction, and Generation
Design effective zero-shot prompts for classification, extraction, and generation tasks.
2
Few-Shot Learning with In-Context Examples
Implement few-shot learning by providing in-context examples within a prompt.
3
Chain-of-Thought Prompting for Multi-Step Reasoning
Apply chain-of-thought (CoT) prompting to guide a model through multi-step reasoning problems.
4
Crafting Effective Prompts: The RCTO Method
Design prompts that explicitly specify role, context, task, and desired output format.
Delimiters & Structured Text for Clearer Prompts
Use delimiters and structured text (e.g., markdown) to improve prompt clarity and model parsing.
6
Iterative Prompt Refinement
Debug and refine failing prompts by analyzing model outputs and iteratively improving instructions.
7
LLM Family Response Comparison
Compare how different LLM families (e.g., OpenAI, Anthropic, Mistral) respond to identical prompts.
8
Prompt Template Library Management
Create and manage a library of prompt templates for reusable and standardized task patterns.
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Training and Scaling Large Language Models
Next module
Advanced Prompting and Context Engineering