Prompt & skill library
5,900 prompts · 1,040 skills · 1,809 free · the rest in 529 packs from €4
2024Sahoo et al. · arXiv 2402.07927
A systematic survey of prompt engineering in large language models
A map of the field: 29 prompting techniques organised by what they are used for, with the models and datasets each was tested on.
Open the paper →2024University of Maryland · June 2024
The Prompt Report — 58 prompting techniques benchmarked
Few-shot + Chain-of-Thought combined is the single highest-performing technique in 2024.
2025Chen et al. · ScienceDirect
Unleashing the potential of prompt engineering for large language models
Domain-specific role prompts reduce hallucinations by 31% in professional contexts.
2025medRxiv · April 2025
Patient education and AI readability across pediatric specialties
Properly structured prompts improved medical content readability scores by 2.4 grade levels.
2023Yao et al. · arXiv 2305.10601
Tree of Thoughts: deliberate problem-solving with large language models
ToT lifts Game-of-24 success from 4% (CoT) to 74% — at 5x token cost.
Open the paper →2023Kojima et al. · NeurIPS
Large language models are zero-shot reasoners
Adding the phrase "Let's think step by step" lifts GSM8K accuracy from 17% to 78.7%.
2022Wei et al. · NeurIPS
Chain-of-Thought prompting elicits reasoning in large language models
CoT raises arithmetic accuracy from 18% to 57% on PaLM 540B; the foundational result.
2024Anthropic · Constitutional AI Series
Many-shot in-context learning with long-context models
Increasing few-shot examples to 100+ continues to lift accuracy on hard reasoning benchmarks.
2024Madaan et al. · arXiv 2303.17651
Self-Refine: iterative refinement with self-feedback
Self-critique loops improve output quality by an average of 20% across 7 generation tasks.
Open the paper →2024Schulhoff et al. · The Prompt Report
A taxonomy of 58 prompting techniques
Of 58 documented techniques, 12 deliver >90% of the practical performance gains.
2025OpenAI · Research Blog
Calibration in large language models
Models can be prompted to produce calibrated confidence scores within 7% of empirical accuracy.
2025Google DeepMind · arXiv preprint
Automated prompt engineering with evolutionary search
Evolved prompts outperformed expert-written prompts on 7 of 10 benchmark tasks.
2017Vaswani et al. · NeurIPS · arXiv 1706.03762
Attention Is All You Need
Introduced the Transformer architecture — self-attention without recurrence — the foundation under every modern large language model.
Open the paper →2020Brown et al. · NeurIPS · arXiv 2005.14165
Language Models are Few-Shot Learners (GPT-3)
Showed that scaling to 175B parameters lets a model learn new tasks from a few in-context examples, with no gradient updates.
Open the paper →2022Wei et al. · NeurIPS · arXiv 2201.11903
Chain-of-Thought Prompting Elicits Reasoning in Large Language Models
Prompting the model to show intermediate steps raises math-word-problem accuracy dramatically on large models — the seminal reasoning result.
Open the paper →2022Kojima et al. · NeurIPS · arXiv 2205.11916
Large Language Models are Zero-Shot Reasoners
Adding "Let's think step by step" alone lifts GSM8K zero-shot accuracy from 17.7% to 78.7% — zero-shot CoT.
Open the paper →2022Wang et al. · ICLR 2023 · arXiv 2203.11171
Self-Consistency Improves Chain of Thought Reasoning in Language Models
Sampling many reasoning paths and majority-voting the answer beats greedy CoT by up to 17.9% on GSM8K.
Open the paper →2022Yao et al. · ICLR 2023 · arXiv 2210.03629
ReAct: Synergizing Reasoning and Acting in Language Models
Interleaving reasoning traces with tool actions reduces hallucination and is the blueprint for modern agent loops.
Open the paper →2023Yao et al. · NeurIPS · arXiv 2305.10601
Tree of Thoughts: Deliberate Problem Solving with Large Language Models
Searching over a tree of partial solutions with self-evaluation lifts Game-of-24 success from 4% (CoT) to 74%.
Open the paper →2022Zhou et al. · ICLR 2023 · arXiv 2205.10625
Least-to-Most Prompting Enables Complex Reasoning in Large Language Models
Decomposing a problem into ordered sub-problems and solving them in sequence generalizes far better to harder instances than standard CoT.
Open the paper →2023Madaan et al. · NeurIPS · arXiv 2303.17651
Self-Refine: Iterative Refinement with Self-Feedback
The same model generates, critiques, and revises its own output — improving quality ~20% on average across seven tasks with no extra training.
Open the paper →2023Shinn et al. · NeurIPS · arXiv 2303.11366
Reflexion: Language Agents with Verbal Reinforcement Learning
Agents store verbal self-reflections after failures and reuse them, reaching 91% on HumanEval — without any model fine-tuning.
Open the paper →2022Press et al. · EMNLP 2023 · arXiv 2210.03350
Measuring and Narrowing the Compositionality Gap in Language Models (Self-Ask)
Having the model ask and answer its own follow-up sub-questions narrows the gap between knowing facts and composing them.
Open the paper →2020Lewis et al. · NeurIPS · arXiv 2005.11401
Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks
Combining a parametric model with a retrieved non-parametric memory sets state of the art on open-domain QA and reduces hallucination.
Open the paper →2022Ouyang et al. · NeurIPS · arXiv 2203.02155
Training Language Models to Follow Instructions with Human Feedback (InstructGPT)
RLHF on a 1.3B model produced outputs humans preferred over the 175B GPT-3 — the method behind modern instruction-following.
Open the paper →2022Zhou et al. · ICLR 2023 · arXiv 2211.01910
Large Language Models Are Human-Level Prompt Engineers (APE)
Automatically generated and selected instructions matched or beat human-written prompts across 24 of 24 benchmark tasks.
Open the paper →2024Schulhoff et al. · arXiv 2406.06608
The Prompt Report: A Systematic Survey of Prompting Techniques
A 58-technique taxonomy from 1,500+ papers; role + few-shot prompting stays consistently top-tier, while several trendy techniques fail to replicate.
Open the paper →2024Sahoo et al. · arXiv 2402.07927
A Systematic Survey of Prompt Engineering in Large Language Models
Organizes 29 prompting techniques by application area, with the models and datasets each was tested on — the quickest map of what exists.
Open the paper →2023Liu et al. · ACM Computing Surveys · arXiv 2107.13586
Pre-train, Prompt, and Predict: A Systematic Survey of Prompting Methods in NLP
The canonical survey framing the shift from fine-tuning to the prompt-based paradigm; defines the vocabulary the field still uses.
Open the paper →2022Wei et al. · TMLR · arXiv 2206.07682
Emergent Abilities of Large Language Models
Certain capabilities appear abruptly only past a scale threshold — context for why prompting techniques work on large models but not small ones.
Open the paper →2022Min et al. · EMNLP · arXiv 2202.12837
Rethinking the Role of Demonstrations in In-Context Learning
Surprisingly, the label correctness of few-shot examples matters less than their format and label space — reframing how to choose exemplars.
Open the paper →2021Wei et al. · ICLR 2022 · arXiv 2109.01652
Finetuned Language Models Are Zero-Shot Learners (FLAN)
Instruction-tuning across many tasks gives a model strong zero-shot generalization to unseen tasks — the basis of instruction following.
Open the paper →About the library
A prompt library of 5,900 structured prompts and 1,040 skills for ChatGPT, Claude and Gemini, organised by field. Every prompt has the same parts — Role, Context, Task, Constraints, Output — so you can see what it does before you copy it. Every title is public; 1,809 prompts and 13 starter skills are free with an account, and every other item comes in a ready pack for one trade or topic — one payment, yours to keep.