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Demand for developers skilled in large language models (LLMs) is growing exponentially, but the pace at which they have been educated has not kept up. This is due to the limited pool of developers with experience in this area, as well as the high cost of training LLMs. However, recent advances in computational techniques and data infrastructure have reduced the costs of training, allowing developers to train and run smaller LLMs with far more modest computational resources. LLM developers require training in several areas, including machine learning, tokenization, token embeddings, transformers, encoder-decoder, decoder-only architectures, autoregressive models, adapters, and policy-based reinforcement learning.