prompt-engineer
SolidLLM prompts - design, evaluate, tune instructions.
Install
Quality Score: 87/100
Skill Content
Details
- Author
- sipyourdrink-ltd
- Repository
- sipyourdrink-ltd/bernstein
- Created
- 4 months ago
- Last Updated
- today
- Language
- Python
- License
- Apache-2.0
Similar Skills
Semantically similar based on skill content — not just same category
prompt-engineer
Use when designing prompts for LLMs, optimizing model performance, building evaluation frameworks, or implementing advanced prompting techniques like chain-of-thought, few-shot learning, or structured outputs.
prompt-engineer
Design, optimize, test, and evaluate prompts for large language models. Use when: (1) crafting or refining system prompts, user prompts, or prompt templates, (2) optimizing token usage or cost of existing prompts, (3) designing few-shot examples or chain-of-thought reasoning, (4) setting up prompt evaluation, A/B testing, or regression testing, (5) building production prompt management systems (versioning, monitoring, safety), (6) debugging inconsistent or low-quality LLM outputs, (7) selecting prompt patterns (zero-shot, few-shot, CoT, ToT, ReAct, role-based). Triggers on: prompt engineering, optimize prompt, reduce tokens, prompt template, few-shot, chain-of-thought, prompt evaluation, A/B test prompts, prompt versioning.
prompt-engineer
Designs, refines, and systematically evaluates LLM prompts using structure, role framing, few-shot examples, explicit output contracts, and reasoning scaffolds. Use this skill when the user wants to write or improve a prompt, build a system prompt, craft few-shot examples, reduce hallucination or refusals, enforce a JSON/structured output, design an LLM-as-judge or eval rubric, debug inconsistent or low-quality model outputs, or compare prompt variants ("optimize this prompt", "why is the model ignoring my instructions", "make it return valid JSON", "write a prompt that...", "evaluate these prompts").