Understand large language models, fine-tuning, prompt engineering, and model selection.
A practical decision framework for choosing between small AI models like Claude Haiku 4.5 or Gemini Flash and flagship models like GPT-5, Gemini 3 Pro, or Claude Opus 4.5.
Learn how system prompts and user prompts differ in AI applications, why system prompts matter for AI behavior control, and how to use both effectively in your implementation.
Deciding between open source AI and custom development? Learn when to use existing models like Llama, Tesseract, YOLO vs building from scratch.
Learn when synthetic data improves AI training and when it creates problems. Real examples from companies using synthetic data for machine learning projects.
Planning to fine-tune an LLM? Learn exactly how much training data you need for different use cases, quality requirements that matter more than quantity, and cost-effective strategies to collect data.
Practical guidance on prompt length limits for AI models. Learn when longer prompts hurt performance, cost implications, and strategies to maintain accuracy while managing context size.
Choosing between prompt engineering and fine-tuning for your AI project? Compare costs, performance, and implementation complexity to find the right approach for your business needs.
Choosing between large LLMs and fine-tuned models? Compare performance, costs, and deployment options to find the right AI approach for your business.
Understand AI, machine learning, deep learning differences. Strategic insights for executives planning enterprise technology investments and implementations.