Understanding the generative AI development process

The generative AI development process involves model selection, prompt engineering, hyperparameter tuning, retrieval-augmented generation (RAG), agents, model fine-tuning, and continued model pre-training. Model selection should consider future updates and ongoing cost. Prompt engineering techniques include providing clear instructions, exemplars, using documents, and utilizing chain-of-density or chain-of-thought prompts. Hyperparameter tuning involves adjusting temperature, context window, maximum number of tokens, and stop sequence. Retrieval-augmented generation helps to ground LLMs with specific sources using embedding and semantic similarity search. Agents expand on conversational LLMs and specialize LLMs to specific domains.