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Everything about Prompt engineering
Structuring text as input to generative artificial intelligence
Prompt engineering is the process of structuring natural language inputs to produce specified outputs from a generative AI model. Context engineering is the related area of software engineering that focuses on the management of non-prompt and prompt contexts supplied to the GenAI model, such as system instructions, metadata, API tools and tokens. Wikipedia →
🧭 Start here: a 6-step reading path
- Read the overview: Prompt engineeringA 5-minute orientation
- Skim a recent paper: Embracing the future of Artificial Intelligence in the classroom: the relevance of AI literacy, prompt engineering, and critical thinking in modern educationInfluential work since 2022
- Pick up a classic book: the power of promptThe most-reprinted book on the subject
- Read the landmark paper: Prompt Engineering in Medical EducationCited 232 times
- Go sideways: Large language modelA closely related topic
- Go sideways: Prompt injectionA closely related topic
📖 Definitions
Noun The technique of formulating instructions (prompts) for a generative AI, in order that it produce the expected output.
🗂️ The subject at a glance
🏆 Landmark papers (most cited)
Prompt Engineering in Medical Education
Prompt Engineering in Healthcare
From Prompt Engineering to Prompt Science with Humans in the Loop
Prompt Engineering for Nurse Educators
Privacy Preserving Prompt Engineering: A Survey
Prompt engineering for healthcare: Methodologies and applications
The prompt engineering librarian
Landscape and Taxonomy of Prompt Engineering Patterns in Software Engineering
🆕 Influential since 2022
Embracing the future of Artificial Intelligence in the classroom: the relevance of AI literacy, prompt engineering, and critical thinking in modern education
Prompt Engineering with ChatGPT: A Guide for Academic Writers
Prompt Engineering as an Important Emerging Skill for Medical Professionals: Tutorial
Engineering Robust Ag‐Decorated Polydopamine Nano‐Photothermal Platforms to Combat Bacterial Infection and Prompt Wound Healing
Prompt engineering in consistency and reliability with the evidence-based guideline for LLMs
📚 Books worth reading
the power of promptAbdualhadi Khalifa · 2023
AI Design Prompts for Viral AdsThe Coach360 · 2026
The Context Window Is a BudgetRavi Vale · 2026
AI · 2025
Claude Code in ActionWes Halloran · 2026
The Magic Words Are DeadRavi Vale · 2026
IA Sans Bullshit 2026Denis atlan · 2025
Ranked by number of editions on Open Library, a rough measure of how lasting a book is.
🔗 Related topics
- Large language model A large language model (LLM) is a machine learning model (typically a neural network) trained on a vast amount of text for natural language processing…
- Prompt injection Prompt injection is a cybersecurity exploit and an attack vector in which innocuous-looking inputs (i.e. prompts) are designed to cause unintended behavior…
- LLM-as-a-Judge LLM-as-a-Judge (also LLM-based evaluation or language model-based evaluation) is a technique in natural language processing in which a large language model…
- Feedback neural network Feedback neural networks are neural networks with the ability to provide bottom-up and top-down approach feedback to their input or previous layers, based…
- Multimodal learning Multimodal learning is a type of deep learning that integrates and processes multiple types of data, referred to as modalities, such as text, audio, images…
- Reasoning model Reasoning language models (RLMs) or large reasoning models (LRMs) are large language models that are trained to solve tasks that require several steps…
- Stance detection Stance detection, also known as stance classification, is a task in natural language processing (NLP) and computational linguistics that uses content analysis…
- Retrieval-augmented generation Retrieval-augmented generation (RAG) is a technique that enables large language models (LLMs) to retrieve and incorporate new information from external…