How to Become a Prompt Engineer

Build a prompt-based chatbot that classifies incoming support tickets by category (billing, technical, account) and urgency level, then generates a draft response for each. Use OpenAI API with few-shot prompting and structured JSON output.

Build a question-answering system over a corpus of PDF documents using LangChain, a vector database (Chroma or Pinecone), and retrieval-augmented generation. Implement chunking strategies, embedding selection, and answer quality evaluation.

Design a LangGraph-based multi-agent system where a planner agent decomposes research questions, researcher agents search the web and databases, a writer agent synthesizes findings, and a fact-checker agent verifies claims. Include human-in-the-loop approval at key stages.

Create an automated evaluation framework that tests 10+ prompt variations against a labeled dataset, scores outputs using LLM-as-judge and rule-based metrics, and visualizes results. Integrate with a CI pipeline so prompt changes are evaluated before merging.

Build a content generation system that produces marketing copy in a specific brand voice, using a system prompt that defines tone, style, and constraints. Implement Guardrails AI or NeMo Guardrails to enforce output structure, detect off-brand language, and prevent competitor mentions.

Build a systematic prompt red-teaming tool that generates adversarial inputs (jailbreaks, prompt injections, bias probes) and evaluates model responses against safety criteria. Include automated scoring, reporting dashboards, and integration with multiple LLM providers.

Design a routing system that classifies incoming queries by complexity (simple, moderate, complex) and routes them to the most cost-effective model (e.g., GPT-4o-mini for simple, GPT-4o for complex) while maintaining quality thresholds. Evaluate cost savings vs. quality trade-offs.