ai-ml pattern
Prompt Engineering
Craft effective prompts to get optimal outputs from Large Language Models. Foundation of AI-powered applications.
Time
Iterative refinement
Space
Token limits vary by model
🧠Mental Model
“Writing a detailed brief for a freelancer - the clearer your instructions, the better the output.”
Verbal cue: Context + Task + Format + Examples = Better output.
🎯Recognition Triggers
When you see these patterns in a problem, consider this approach:
LLMChatGPTAI assistantpromptgenerate text
💡Interview Tips
- 1Know the difference between zero-shot, few-shot, and chain-of-thought
- 2Mention temperature and its effect on creativity vs consistency
- 3Be aware of prompt injection vulnerabilities
⚠️Common Mistakes
- ✕Being too vague - LLMs need specifics
- ✕Not specifying output format
- ✕Ignoring token limits and context windows