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Tim Gardner Webinar 2 2025
02:11:58
Python data types: Collections.UserString
00:05:03
Does using LLMs help or hinder learning?
00:02:18
Should Students Disclose AI Use in Assignments?
00:02:00
Plagiarism, paraphrasing, and prompting
00:02:13
What counts as cheating? LLMs and Academic Integrity
00:02:06
When Not to Trust the Output – Hallucinations and Safety
00:02:14
The Bias Through Prompt Crafting
00:02:20
Designing Prompts With Ethics in Mind
00:02:13
Prompt Induced Harm
00:02:03
Bias in LLMs
00:02:21
Prompt Robustness and Consistency Challenges
00:02:30
AB Testing Prompts – Empirical Iteration
00:02:04
Debugging Prompts – A Practical Workflow
00:02:09
Common Prompting Errors and How to Fix Them
00:03:27
Evaluating LLM Output   Quality Metrics
00:02:16
React Prompting   From Prompt to Agent
00:01:49
Retrieval Augmented Generation (RAG) Basics
00:02:14
Introduction to Function Calling in Prompts
00:02:25
Using System Prompts in Chat Based Models
00:03:53
Prompt Chaining
00:02:15
Formatting Prompt Outputs
00:02:11
Few Shot Prompting
00:02:08
Constraint Based Prompting
00:01:50
Chain of Thought Prompting
00:02:11
AI Role Prompting
00:01:57
Instruction, Input, Context—A Framework for Prompt Design
00:02:43
Prompt Clarity—Avoiding Ambiguity and Misfires
00:01:50
How Tokenization Affects Your Prompt
00:01:58
Zero-Shot vs  One-Shot vs  Few-Shot Learning
00:02:02