Why so many LLM projects fail before they begin

A new educational initiative aims to address the foundational knowledge gap in large language model (LLM) development by providing a comprehensive, practical breakdown of how LLMs generate outputs, reason, and fail, focusing on core processes such as tokenization, embeddings, attention mechanisms, and autoregression. This initiative emphasizes understanding the underlying mechanics to improve reliability and troubleshoot issues like hallucinations, bias, and context limitations, which are often misunderstood or overlooked by developers relying solely on tools like RAG templates or fine-tuning. By highlighting common pitfalls such as prompt injection, data leakage, and cascading failures, the program