llm-prompt-optimizer

name	llm-prompt-optimizer
description	Use when improving prompts for any LLM. Applies proven prompt engineering techniques to boost output quality, reduce hallucinations, and cut token usage.
type	skill
created	2026-02-27T00:00:00.000Z
domain	ai-ml
category	prompt-engineering
risk	safe
source	community
tags	["skill","ai-ml","prompt-engineering","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 should have:

• R — Role: Who is the AI in this interaction?
• S — Situation: What context does it need?
• C — Constraints: What are the rules and limits?
• I — Instructions: What exactly should it do?
• T — Template: What should the output look like?

Before (weak prompt):

Explain machine learning.

After (optimized prompt):

You are a senior ML engineer explaining concepts to a junior developer.

Context: The developer has 1 year of Python experience but no ML background.

Task: Explain supervised machine learning in simple terms.

Constraints:
- Use an analogy from everyday life
- Maximum 200 words
- No mathematical formulas
- End with one actionable next step

Format: Plain prose, no bullet points.