Training Data

The dataset used to train machine learning models, consisting of input-output pairs that define the model’s learning patterns. Quality, diversity, and scale directly determine model performance and bias.

• Key aspects:
  • Supervised learning requires labeled examples
  • Data bias can propagate to model outputs
  • Data augmentation techniques expand effective dataset size
  • Ethical AI considerations require careful data curation

Recent Reviews:

• Dave’s Garage - review of AI models (2026-04-14): Dave Plummer (retired Microsoft engineer) notes mid-2025 LLM landscape has evolved beyond ChatGPT-4 dominance, with Grok-3 and Gemini now competitive models trained on increasingly diverse datasets.

2026 04 14 Daves Garage review of AI models

Source Notes

• 2026-04-23: https://www.youtube.com/watch?v=-AJoByRGkgU The speaker, Dave Plummer, a retired Microsoft software engineer, provides an opinionated look at the current state of AI Large Language Models (LLMs) as of mid-2025. He has subscribed to and heavily used the top four models: ChatGPT, (Dave’s Garage review of AI models)
• 2026-04-23: https://www.youtube.com/watch?v=JTbtGH3secI This video, titled “Why Your AI Models Are Hallucinating & How to Fix Them,” provides a comprehensive overview of the phenomenon of “hallucination” in Large Language Models (LLMs) and, more importantly, details practical strategies to (Prompt Engineering Local GPT for RAG)
• 2026-04-14: # Difference between RAG and Agents for workflow --- --- https://www.youtube.com/watch?v=WYqhc802nqk Here is a detailed breakdown of the video “RAG vs Agents” by Dr. Anil Variyar. Video Summary Dr. Anil Variyar provides a vi (Difference between RAG and Agents for workflow)