AI Personal AI Assistant Developer vs Prompt Engineer

B Prompt Engineer Only

• LLM behavior analysis - understanding how models interpret instructions, context, and constraints
• Prompt pattern design - zero-shot, few-shot, chain-of-thought, ReAct, tree-of-thought, and self-consistency techniques
• Retrieval-Augmented Generation (RAG) architecture - chunking strategies, embedding selection, and context injection
• Evaluation and benchmarking - building scoring rubrics, automated evals, and human-in-the-loop testing
• Token economics and cost optimization - managing context windows, compression, and model selection trade-offs
• Python scripting for orchestration - using LangChain, LlamaIndex, or raw API calls to chain prompts programmatically
• Structured output engineering - JSON mode, function calling, tool-use schemas, and output parsing
• Version control and prompt management - tracking prompt iterations with PromptLayer, LangSmith, or Git-based workflows