TOPIC AI
Prompt Engineering crafting inputs to get reliable, structured outputs from LLMs
IN 10 SECONDS
The way you phrase a prompt dramatically affects the quality and format of an LLM response. Good prompt engineering uses clear instructions, few-shot examples (showing the model what you want), role-setting ('You are a senior DevOps engineer'), and output formatting (JSON, markdown). Systematic approaches like chain-of-thought improve reasoning tasks.
GOTCHA Small prompt changes can produce wildly different outputs. A/B test prompts systematically — change one variable at a time and measure response quality. Use prompt versioning in your codebase, not ad-hoc tweaks in a chat UI.
HOW A PROMPT IS STRUCTURED
01 System message sets the model's persona and constraints — e.g., 'You are a Terraform expert. Answer concisely.'
02 Few-shot examples provide 1-3 examples of ideal Q&A pairs to demonstrate format, tone, and depth.
03 Context injection retrieved documents, schema definitions, or API docs are inserted as grounding material.
04 User query the actual question or task, often with explicit instructions like 'respond in JSON with keys: summary, steps, risk.'
05 Output parsing the response is validated against the expected format and error-handled if the LLM deviates.
POKE IT YOURSELF
ollama run llama3 'Write a prompt that...' — test prompts via Ollama locally
curl -X POST -d '{"model":"gpt-4","messages":[{"role":"system","content":"You are a terse Linux expert"}]}' https://api.openai.com/v1/chat/completions — call OpenAI with a system message
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