Lost Midas
@lostmidas
Prompting for people who actually want results Anyone can throw words at an LLM Few build prompts that scale & deliver - Define the role: "You are a [role] who [does X]" sets clear context - Break tasks into steps: Step-by-step reduces model drift & keeps things accurate - Use XML tags: Structure matters, LLMs like XML - Specify output format: Tell it what you want, structure isn’t optional - Use Markdown: Headers & bullets make prompts readable - Metaprompt for help: Show the model failed outputs, let it help you debug - Prompt folding: Roll up past instructions into clear meta-prompts - Example-based prompts: Show edge cases & ask for pattern matching - Escape hatch: Let the model admit it doesn’t know, avoid forced answers - Debug field: Add 'debug_info' to catch unclear logic or confusion
1 reply
0 recast
4 reactions
Lost Midas
@lostmidas
- Three-tier prompt stacks: System → Dev → User layers let you scale cleanly - Distill prompts with bigger models: Use Claude 3 to refine prompts for smaller models - Reasoning traces: Test one example at a time & watch for wrong turns - Auto-example generation: Pull real customer data into prompts automatically - Scoring rubrics: Use a 0–100 scale for objective evaluation - Choose your model wisely: GPT-4 = rules-focused; Gemini = flexible; Claude = empathetic - Iterate in a doc: Log failures & feed your learnings back to the model - Fork by customer: Base prompt → client variants, no need for total rewrites - Scale with auto-examples: Inject real-world examples often - Validate with real data: Debug output isn’t noise, it’s your to-do list Master these & your AI agents stop guessing & start delivering
0 reply
0 recast
1 reaction
