How to Learn AI in 2026: A Hands-On Guide from First Prompt to Shipping Agents
A practical, step-by-step guide to learning AI in 2026. Real prompts, real commands, real exercises — from your first Claude conversation to building autonomous agent loops. No fluff, no theory walls.
This is not a survey of AI tools. It is a guide you can open right now, follow step by step, and be measurably better at using AI by the end of each section.
Every section has a thing to do, a thing to copy, or a thing to build. If a section has none of those, it shouldn't be in a guide.
Before anything: open Claude right now
Go to claude.ai and sign up if you haven't. You will need it open alongside this guide. Every exercise below assumes you have a chat window in front of you.
Stage 1: Your first real prompt (Day 1)
Most people's first AI interaction is a test question — "what is the capital of France" or "write me a poem." That teaches you nothing about how to use AI for real work.
Exercise 1 — summarise something you actually need to read
Find a long document you've been putting off. A long article, a contract, a research paper, a competitor's product page. Paste the whole thing into Claude. Then send this exact prompt:
snippet
Summarise this in 5 bullet points. Then tell me the 3 most important things I should pay attention to, and flag anything that seems unusual or that I should be cautious about.
[paste your document here]
Read the output. Notice:
Did it miss anything important?
Did it say something confident that seems wrong? (That's a hallucination — you'll learn to spot them.)
Does the framing match what the document is actually about?
Now ask a follow-up without re-pasting the document:
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What are the implications of point 2 for someone in [your role]?
You just did the core AI feedback loop: give context → get output → evaluate → ask follow-up. Everything else in this guide is an elaboration of that loop.
Exercise 2 — rewrite something you wrote last week
Find an email, a message, or a paragraph you sent recently. Paste it and use this prompt:
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Rewrite this to be more direct and 30% shorter. Keep the same meaning and tone. Don't add filler phrases like "I hope this finds you well."
[paste your text]
Compare the output to your original. Where did it cut correctly? Where did it miss the point? This tells you how much context you implicitly carry that you didn't write down. That gap — what you know but didn't say — is the central problem of prompt engineering.
Stage 2: Writing prompts that actually work (Week 1–2)
The difference between a good and bad prompt is almost never vocabulary. It's structure. Here is the structure that produces consistent results:
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Role: [who Claude should be for this task]
Context: [what Claude needs to know about the situation]
Task: [what you want, precisely]
Format: [how the output should be structured]
Constraints: [what to avoid]
You don't need all five every time. But knowing all five means you know what's missing when results are bad.
Bad prompt:
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Write a cold email for my SaaS product
Good prompt:
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Role: You are a senior B2B copywriter who specialises in short-form outreach.
Context: I run a project management tool called Trackr. It's built for remote engineering teams of 5–20 people. The main pain we solve is async standups — teams waste 45 minutes every morning in a Zoom standup that could be a 3-minute async update. Our biggest competitor is Linear. We're cheaper and have better notifications.
Task: Write a cold email to a Head of Engineering at a 30-person remote startup. The goal is to get a 20-minute call, not to sell immediately.
Format: Subject line + 4-sentence body. No bullet points. No more than 100 words total.
Constraints: Don't use the words "seamless", "streamline", or "unlock". No exclamation marks. Don't open with "I hope this email finds you well."
Exercise 3 — fix one of your bad prompts
Take something you've tried to get AI to do that came out mediocre. Apply the five-part structure. Run both prompts and compare the outputs.
Keep both versions. The gap in output quality is your progress indicator.
Exercise 4 — the chain of thought trick
When you need reasoning, not just output, add this to the end of your prompt:
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Think through this step by step before giving your final answer.
Try it on a decision you're currently wrestling with at work:
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I need to decide whether to [your actual decision]. Here are the relevant factors: [list them].
Think through this step by step before giving your final answer. After the reasoning, give me a clear recommendation and the one assumption I should pressure-test first.
Stage 3: System prompts — your most powerful lever (Week 2–3)
A one-off prompt is a question. A system prompt is a configuration. It shapes every response in a conversation, so you stop starting from zero every time.
Go to claude.ai/projects. Create a project. Set a system prompt. Now every conversation in that project starts from your configured baseline.
A system prompt for a content writer:
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You are my content research and drafting assistant. I write about B2B SaaS, primarily for a technical audience (developers and engineering leaders).
Your defaults:
- Write in plain English. Short sentences. No jargon unless I use it first.
- When you make a market claim, say where it comes from or flag that it's your inference.
- When I paste competitor content, start by telling me what they're doing well before any critique.
- If you're uncertain about a fact, say so explicitly rather than presenting speculation as fact.
- Don't add unnecessary qualifiers like "certainly" or "absolutely" — they read as filler.
When I ask for a draft, give me one version and note the main trade-off I might want to reconsider.
A system prompt for a developer:
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You are my coding assistant. My stack: TypeScript, Next.js App Router, Prisma, PostgreSQL, Tailwind v4.
Your defaults:
- When writing code, match the patterns already in the file I paste. Don't introduce new abstractions unless I ask.
- Always explain WHY when the solution is non-obvious. Skip explanations when it's standard boilerplate.
- If my approach has a better alternative, mention it once, then do what I asked.
- Flag potential security issues immediately. Never skip this.
- If I paste an error, diagnose the root cause before suggesting a fix.
A system prompt for a manager:
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You are my thinking partner for business decisions. I'm a product manager at a 80-person B2B startup.
Your defaults:
- When I describe a problem, ask me one clarifying question before advising if the situation is ambiguous.
- Give me the strongest case against my initial position as well as the case for it.
- When I'm venting, reflect back what you heard before offering any analysis.
- Keep responses concise — I read fast and I'll ask follow-ups if I want more.
Exercise 5 — write your own system prompt
Write a system prompt for your actual job. The template:
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You are my [role] assistant. I work as a [your role] at a [company type].
My context:
- [1-3 facts about your domain the AI needs to know]
- [the tools/stack/vocabulary specific to your world]
Your defaults:
- [behaviour 1]
- [behaviour 2]
- [behaviour 3]
Things to avoid:
- [thing 1]
- [thing 2]
Set this in a Claude project. Use it for a week. You'll discover what's missing from the prompt by noticing where the output drifts from what you want.
Stage 4: Workflows — linking prompts into repeatable systems (Week 3–4)
A workflow is a sequence of prompts where the output of each step feeds the next. You run it repeatedly on different inputs and get consistent, reliable output.
Example: weekly competitor monitoring workflow
Step 1 — Extraction prompt (you paste a competitor blog post):
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Extract from this article:
1. The main argument or claim they're making
2. Any stats or data points they cite (with the source if mentioned)
3. The implied customer pain they're addressing
4. What they're NOT saying (gaps or assumptions)
Be concise. Use bullet points under each heading.
Step 2 — Comparison prompt (you paste your own positioning doc):
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Given the competitor analysis above and my positioning doc below, answer:
1. Where are they going after the same customer pain we address?
2. Where are they differentiated from us?
3. Is there anything in their framing we should steal or counter?
My positioning: [paste doc]
Step 3 — Monthly synthesis prompt:
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Below are 4 weeks of competitor analyses. Synthesise into:
- The 2-3 themes they keep returning to
- Any shift in positioning vs last month
- The single most important thing I should pay attention to
[paste 4 weeks of outputs]
Exercise 6 — build one workflow this week
Pick a task you do repeatedly where the output is variable (writing reports, analysing feedback, reviewing applications, researching topics). Break it into 2–3 steps. Write a prompt for each step. Run it three times on real inputs. Refine the prompts based on where it breaks.
By the third run, you should be editing outputs rather than starting from scratch.
Stage 5: Claude Code — building with AI (Week 4–6)
Claude Code is a command-line agent that reads your codebase and builds things with you. You don't need to be a developer. You need a terminal and the ability to describe what you want in English.
cd ~/Desktop
mkdir my-first-ai-project
cd my-first-ai-project
claude
Exercise 7 — your first real Claude Code session
Once you're in the Claude Code prompt, type this:
snippet
Create a simple HTML page that lets me paste in a block of text and converts it into a numbered list of bullet points, one sentence per line. It should work offline with no external dependencies.
Watch it write the HTML, CSS, and JavaScript. When it's done, type:
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Now add a "copy to clipboard" button that copies the formatted list
You just built a tool. It took under 5 minutes. The skill you are practising is not coding — it is describing what you want precisely enough for an agent to build it.
What to practice in Claude Code:
Replace vague requests with specific ones. Compare:
Vague
Specific
"make it look better"
"make the font Geist Mono, add 16px padding, set the background to #09090b"
"fix the bug"
"when I click submit with an empty field, the page crashes — prevent submission and show an inline error message under the input"
"add a feature"
"add a character counter below the textarea that shows characters remaining out of 500, turning red when under 50"
Exercise 8 — build something you'd actually use
Pick one of these and build it in a single Claude Code session:
A CSV to formatted HTML table converter
A word frequency counter for a pasted article
A Markdown-to-email formatter that strips code blocks and preserves structure
A simple timer with a sound alert (pick any tone)
Don't touch the code yourself. Describe what you want. When something doesn't work, describe what you expected vs what happened. Ask it to fix it.
An MCP (Model Context Protocol) server is a connector that gives an AI access to a tool or data source. With an MCP server installed, Claude can read your GitHub repos, write to your Notion workspace, query your database, search the web, or call any API — with your permission.
Think of your AI as a very capable person who was locked in a room with only a keyboard. MCP servers are doors to the outside world.
Install your first MCP server — GitHub:
bash
claude mcp add github npx @modelcontextprotocol/server-github
Set your GitHub token:
bash
export GITHUB_TOKEN=your_github_token
Now inside a Claude Code session:
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List my last 5 open pull requests across all my repos
Claude reads your GitHub directly and answers from real data.
Install the filesystem server (read/write local files):
bash
claude mcp add filesystem npx @modelcontextprotocol/server-filesystem /Users/yourname/Documents
Now Claude can read and write files in that directory. Try:
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Read every .txt file in this folder and create a single summary document that organises the key points by theme
Browse the full MCP server directory at explainx.ai/mcp-servers — 500+ servers for Slack, Notion, Linear, Stripe, databases, web search, and more.
Exercise 9 — connect Claude to one tool you use daily
Pick one: Notion, GitHub, Slack, Google Drive, your database. Install the MCP server. Then run a real task against it. Not a test task — an actual piece of work you would otherwise do manually.
Examples:
"Summarise all Notion pages updated this week and list anything that has an open question or decision needed"
"Find all GitHub issues labelled 'bug' with no assignee and draft a triage comment for each"
"Read the last 50 messages in the #general Slack channel and tell me if there's anything I need to respond to"
A skill is a saved instruction set that an AI agent can call as a unit. Instead of re-explaining how to do something every session, you write it once as a skill and the agent loads it when needed.
Skills live in .skill.md files. Here is what one looks like:
markdown
# Competitor Analysis Skill## Purpose
Analyse a competitor's content to extract positioning signals, cited data, and gaps.
## Input
A URL or pasted article from a competitor.
## Steps1. Identify the primary claim or argument
2. List all cited statistics with their sources (mark "unverified" if no source given)
3. Identify the implied customer pain the content addresses
4. Note what is NOT addressed — gaps, assumptions, avoided topics
5. Score threat level: Low / Medium / High, with one sentence of reasoning
## Output format
Use this structure:
-**Main claim:** [one sentence]
-**Stats cited:** [bullet list]
-**Pain addressed:** [one sentence]
-**Gaps:** [bullet list]
-**Threat level:** [Low/Medium/High — reason]
Exercise 10 — write your first skill
Pick something you've prompted Claude to do more than three times. Write it as a skill file. The format:
markdown
# [Skill name]## Purpose
[One sentence: what this skill does]
## Input
[What you give it]
## Steps
[Numbered list of what the agent does with the input]
## Output format
[Exactly how the result should be structured]
Save it as [skill-name].skill.md in your project folder. Point Claude Code at it:
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Use the competitor-analysis skill to analyse this article: [paste article]
Browse 2,000+ community skills at explainx.ai/skills. Install them into your workflow instead of writing from scratch.
Stage 8: Agent loops — AI that runs without you (Month 3–5)
An agent loop is a system where the AI takes an action, observes the result, decides what to do next, and continues — without you prompting each step.
The simplest possible loop in Claude Code:
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Every morning, read the TASKS.md file in this folder. For each uncompleted task:
1. Determine what type of task it is (research / write / code / review)
2. Complete the task using the appropriate approach
3. Write the output to a new file named [task-name]-output.md
4. Mark the task as complete in TASKS.md
Start now with the first three tasks.
Create a TASKS.md with three real tasks. Run this. Watch it work through them.
The four loop patterns worth knowing:
1. Research loop — for gathering and synthesising information:
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Search for the 5 most recent news stories about [topic].
For each: extract the key claim, note the source credibility, and flag any contradictions between sources.
After all 5: write a 150-word synthesis identifying what is confirmed, what is contested, and what is unknown.
Repeat with the next 5 stories until you have processed 20 total.
2. Validation loop — for checking your own outputs:
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Write a first draft of [thing].
Then review it against these criteria: [list].
Identify which criteria are not fully met.
Revise the draft to address each gap.
Repeat review-revise until all criteria are met or you've done 3 rounds.
Show me the final version and a summary of what changed between rounds.
3. Monitoring loop — for watching a data source:
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Check the [data source] every time I run this.
If [condition], write a summary to alerts.md with the date and what triggered it.
If no condition is met, write "No alerts — [date]" to the log.
4. Orchestration loop — for breaking large tasks into parallel subtasks:
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I need to produce [large output]. Break this into [N] independent subtasks.
List the subtasks first and wait for my approval.
Then complete each subtask and combine the outputs into a final document.
Exercise 11 — run a real loop
Pick the research loop. Give it a topic you genuinely need to understand better — a competitor, a market, a technology. Let it run on 10 sources. Read the synthesis it produces.
The synthesis will be imperfect. That's expected. Your job at this stage is to evaluate it: what did the loop get right, what did it miss, and what would you add to the prompt to improve the next run.
What makes a loop reliable vs fragile:
Fragile
Reliable
No checkpoint — runs to completion or fails silently
Writes progress to a log file at each step
One large task
Broken into small, verifiable subtasks
No error handling
"If you can't complete a step, write WHY to errors.md and continue with the next task"
Irreversible actions without confirmation
"Before [irreversible action], show me what you plan to do and wait for me to type CONFIRM"
Add the reliable patterns to every loop you build.
What to build at each stage
Rather than more theory, here's a concrete project for each stage:
Stage
Build this
1 — First prompt
Summarise 3 articles in your field using the Stage 1 prompt
2 — Structured prompting
Write 10 prompts for the 10 most common tasks in your job
3 — System prompts
Configure 2 Claude Projects with role-specific system prompts
4 — Workflows
Build a 3-step research workflow for a report you write regularly
5 — Claude Code
Build a tool you use at least once a week
6 — MCP
Connect Claude to one external tool and run a real task
7 — Skills
Write 3 skills for tasks you do more than weekly
8 — Loops
Build a monitoring or research loop that runs unattended
The prompts you'll use every day
Save these. They cover 80% of practical AI work.
Summarise with priorities:
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Summarise this in 5 bullets. Then flag the 2 most important points and anything I should be cautious about.
Improve my writing:
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Rewrite this to be clearer and more direct. Cut any redundant sentences. Keep the tone [formal/conversational/technical]. Don't change the meaning.
Think through a decision:
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I need to decide [decision]. Context: [relevant facts]. Think through this step by step. Give me a recommendation and the one assumption I should pressure-test first.
Devil's advocate:
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I'm planning to [your plan]. Give me the strongest case against this, assuming I'm wrong.
Explain like I'm new to this:
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Explain [concept] as if I understand [adjacent concept] but have never heard of this one. Use an analogy from [my domain].
Write a first draft:
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Write a first draft of [thing]. Audience: [who]. Goal: [what should they do/feel/think after reading]. Length: [approximate]. Don't optimise for polish — I'll edit it.
Debug this:
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This isn't working as expected. What I expected: [X]. What actually happened: [Y]. Here's the relevant [code/data/text]: [paste]. What's the most likely cause?
Extract structure from a mess:
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I'm going to paste some [meeting notes/emails/research]. Extract: (1) decisions made, (2) action items with owners if mentioned, (3) open questions. Ignore everything else.
Workshops that skip months of trial and error
These are live sessions where you build, not watch:
Update — July 10, 2026: OpenAI's ChatGPT for Work Academy is the best free async map — explainx.ai live workshops are the best cohort path for the same roles.
Claude for Work — Aug 1–2, 2026. System prompts, prompt libraries, and AI workflows for professionals. 2 hours × 2 sessions.
Loop Engineering — July 20, 2026. Build agent loops that run unattended. 4-hour hands-on session.
AI Skills — Sep 12–13, 2026. Install agent skills and author your own SKILL.md — for founders, marketers, PMs, and FDEs. No code required. 2 hours × 2 sessions.
MCP Bootcamp — Oct 31–Nov 1, 2026. Configure MCP in Claude Code, Cursor & Claude.ai, build a custom server, ship live integrations. For developers and technical builders. 2 hours × 2 sessions.
The fastest way to get better is to run more sessions on real tasks. Not tutorials. Not videos. Open Claude, pick something you actually need to do today, and do it with AI.
Update — July 6, 2026: New research backs the "do, don't just read" advice above — see Dartmouth's Phosphor study, where AI-graded written practice drove real learning gains and passive chatbot use didn't.
