llm-prompt-optimizer

LLM Prompt Optimizer

Overview

This skill transforms weak, vague, or inconsistent prompts into precision-engineered instructions that reliably produce high-quality outputs from any LLM (Claude, Gemini, GPT-4, Llama, etc.). It applies systematic prompt engineering frameworks — from zero-shot to few-shot, chain-of-thought, and structured output patterns.

When to Use This Skill

• Use when a prompt returns inconsistent, vague, or hallucinated results
• Use when you need structured/JSON output from an LLM reliably
• Use when designing system prompts for AI agents or chatbots
• Use when you want to reduce token usage without sacrificing quality
• Use when implementing chain-of-thought reasoning for complex tasks
• Use when prompts work on one model but fail on another

Step-by-Step Guide

1. Diagnose the Weak Prompt

Before optimizing, identify which problem pattern applies:

Problem	Symptom	Fix
Too vague	Generic, unhelpful answers	Add role + context + constraints
No structure	Unformatted, hard-to-parse output	Specify output format explicitly
Hallucination	Confident wrong answers	Add "say I don't know if unsure"
Inconsistent	Different answers each run	Add few-shot examples
Too long	Verbose, padded responses	Add length constraints

2. Apply the RSCIT Framework

Every optimized prompt sho