What is AI prompting and how has it changed over time?
AI prompting is the art of writing instructions that guide artificial intelligence models (like ChatGPT, Gemini, Copilot or Claude) to generate useful answers. Between 2019 and 2025, prompting evolved pretty significantly from simple “one-shot” requests into powerful systems that support reasoning, memory, and tool-calling.
This article is a timeline of AI prompting methods, explained in plain English with examples. We’ll cover:
- How prompting techniques like zero-shot, one-shot, few-shot, chain-of-thought, and persona prompts changed the way we interact with AI.
- The rise of reasoning models, retrieval-augmented generation (RAG), memory, and multimodal prompts.
- What beginners can still learn today about writing better prompts in 2025, even as AI systems handle much of the complexity for you.
Whether you’re a beginner asking “How do I write a good AI prompt?” or you’ve been experimenting since the early days, this timeline will show you exactly how prompting got us here – and what still matters now.
TLDR;
We started simple, got complicated and lengthy, now we’re back to simple again.
The Evolution of AI Prompting (2019–2025)
From one-shot instructions to agentic, tool‑calling systems. A visual timeline with examples you can reuse.
2019 · Zero‑Shot Prompting
Ask Directly, No Examples
You give a clear instruction and the AI answers with no examples or extra context. Works best for simple, well‑known tasks.
Example: “Write a 3‑sentence bedtime story about a dragon who learns to share.”
2020 · One‑Shot Prompting
Show One Example, Then Ask
Provide a single example to set format or tone, then make your request.
Example: “Example caption: ‘5 quick dinners that don’t wreck your budget.’ Now write a caption for a productivity post.”
2020 · Few‑Shot Prompting
Give a Pattern with a Few Examples
Show several examples so the model learns the style or schema before your task.
Example: “Examples:
• Tagline → ‘Sleep better with small habits.’
• Tagline → ‘Plant‑based meals, zero fuss.’
Now: Tagline for a time‑management app.”
2021 · Persona Prompting
Ask the Model to Role‑Play
Set a perspective or communication style by assigning a role. ‘Act as a [X]’
Example: “Act as a friendly fitness coach. Create a 20‑minute no‑equipment routine for beginners.”
2022 · Chain & Tree of Thought
Show Your Working (One Path or Many)
Chain‑of‑Thought explains step‑by‑step logic. Tree‑of‑Thought explores several solution paths before choosing one.
Example: “Plan a one‑week budget trip to Paris. Think step by step about transport, accommodation, free activities, and daily meals. Offer two alternate itineraries and pick the best.”
2022 · Iterative Prompting
Refine in Loops
Use your previous output as input. Ask for edits, constraints, or new angles until it’s right.
Example: “Draft a LinkedIn post announcing a webinar.”
“Now make it more benefit‑focused. “
“Now shorten to 150 characters.”
2023 · Self‑Consistency
Generate Several, Keep the Best
Ask for multiple answers, then choose or vote for the most consistent or plausible one.
Example: “Give three solutions for reducing meeting overload. Then explain which one likely has the highest impact and why.”
2023 · Context Prompting & RAG
Ground Answers in Your Material
Paste key context or connect retrieval so the model cites and summarises what matters.
Example: “Here are last week’s meeting notes [paste]. Summarise decisions and list owners + deadlines.”
2023 · Meta, Reflexion & ReAct
Prompts About Prompts, Plus Reason & Act
Meta generates better prompts. Reflexion critiques and revises. ReAct mixes reasoning with tool use.
Example: “Propose five prompt phrasings to get a clear, bulleted onboarding checklist. Then pick the best and produce the checklist using the Notes MCP tool”
2024 · System Prompts & Reasoning Models
Quality by Default
Invisible system instructions handle tone and structure. Reasoning models plan, critique, and solve multi‑step tasks without prompt hacks.
Example: “Create a project plan for launching a newsletter. Include milestones, owners, risks, and a two‑week timeline.”
2024 · Memory & Source Checking
Long‑Running Tasks, Fewer Hallucinations
AI remembers past sessions and cites sources. Better for ongoing projects and trust.
Example: [Based on our previous sprint notes] “At last weeks sprint were there any carried‑over tasks? Can you link to any relevant docs.”
2025 · Tool‑Calling, MCP & Multimodal
From Words to Workflows
Prompts can invoke tools and APIs, and combine text with images, audio, or files. Tasks become orchestrated workflows.
Example: “Review this kitchen photo, propose a redesign, and output a shopping list as a table with estimated costs.”
2025 · Where We Are Now
Simple Prompts, Smarter Systems
Modern models ship with robust system prompts, reasoning, and retrieval. Beginners can get strong results with a single, clear request.
Example: “Write a 6‑page bedtime story with pictures for Josh about a different dragon who learns to share.”
2025 – Where We Are Now
We are back to the beginning.
By September 2025, prompting is less about clever tricks and personas and more about clear communication and having some form of understanding of the models capabilities.
Modern models:
- Already come with great baked-in system prompts.
- Can reason, critique, and fact-check.
- Work with images, audio, and tools.
- Know you, your ‘history’ and can access files, memories or other helpful context without being told.
The DNA of a Modern AI Prompt: Key Takeaways
- Clarity: Start with a clear, direct, and unambiguous instruction.
- Context & Examples: Ground the AI by providing relevant background information or a few examples (few-shot) to guide its output.
- Constraints & Persona: Define the “box” the AI should think inside by setting a format, tone, length, or persona.
- Reasoning: For complex tasks, encourage step-by-step thinking (Chain-of-Thought) to improve logical accuracy.
- Iteration: Use the AI’s output as input for follow-up prompts, refining the result in a conversational loop.
- Tools & Data: Leverage modern systems that can access external knowledge (RAG) or perform actions (Tool-Calling) for the most powerful results.
Frequently Asked Questions
>What is the difference between zero-shot, one-shot, and few-shot prompting?
Zero-shot prompting is giving a direct instruction to an AI with no examples. One-shot prompting provides a single example to set the tone or format. Few-shot prompting gives several examples to teach the AI a specific pattern or schema before it performs the task.
>What is Chain-of-Thought (CoT) prompting?
Chain-of-Thought (CoT) prompting is a technique where you instruct the AI model to ‘think step by step’ or show its reasoning process. This breaks down complex problems into logical parts, often leading to more accurate and reliable answers, especially for multi-step tasks.
>How does Persona Prompting improve AI responses?
Persona Prompting improves AI responses by assigning the model a specific role or character (e.g., ‘Act as a friendly fitness coach’). This sets a clear perspective, tone, and communication style, making the output more tailored and effective for a specific audience or purpose.
>What are modern prompting techniques like RAG and Tool-Calling?
Retrieval-Augmented Generation (RAG) is a technique where the AI is grounded in specific, provided context (like your own documents) to reduce hallucination and provide source-based answers. Tool-Calling allows a prompt to invoke external tools and APIs, enabling the AI to perform actions, get live data, or orchestrate complex workflows beyond simple text generation.
>What has been the main goal of the evolution in AI prompting?
The main goal has been to move from simple instructions to complex, reliable workflows. The evolution has focused on increasing the AI’s accuracy, reducing errors (hallucinations), enabling it to solve multi-step problems, grounding it in factual data, and allowing it to interact with external systems. This makes AI more useful for practical, real-world tasks.
AI prompting has evolved, but these fundamentals remain timeless.
The principles of a good prompt and the right amount of added context still matter.
Though modern frontend AI interfaces and models have given us a much more intelligent starting place. AI is becoming more user friendly, especially for beginners or occasional users.