Practical guides for ChatGPT and Claude, copy-paste prompts, automation blueprints and no-code builds. Everything you need to turn AI tools into working systems.
ChatGPT & Claude, end to end.
Copy-paste patterns that hold up.
Make, n8n and Zapier blueprints.
Real tools, no backend written.
"You don't need to learn to code. You need a prompt that works, a workflow that runs, and a tool stack that connects them."
AIStack is a practical library for people who use technology to get work done: step-by-step guides for ChatGPT and Claude, a tested prompt collection, automation blueprints, and no-code build walkthroughs.
It is not a newsletter full of AI news. It is a set of workflows you can copy, adapt, and run today — with the exact prompts, tools, and connections spelled out.
Every guide gives you one outcome, one tool stack, and one repeatable workflow — clear enough to rebuild in an afternoon.
Whether you're drowning in manual tasks, unsure which model to use, or stuck copy-pasting between tabs — AIStack shows you how to wire it together properly.
Works across ChatGPT, Claude, Gemini, Make, n8n, Zapier, Airtable, and Notion — you pick the stack you already pay for.
Every blueprint comes from automations actually running in small teams, agencies, and solo businesses.
Each guide is scoped so you can go from blank screen to a working automation in a single focused session.
Key Idea
A single well-built automation can remove 5+ hours of repetitive work per week — and it usually takes one afternoon to set up.
For people who are done watching AI demos and want the actual prompts, tools, and connections behind them.
Replace manual ops with automated flows. Handle onboarding, reporting, and follow-ups without hiring for it.
Turn one asset into a month of content, enrich leads automatically, and build client dashboards without a developer.
Prompt systems that keep your voice intact — research, drafting, editing, and repurposing across every channel.
Use Claude's long context to summarize papers, build study material, and check reasoning instead of trusting one answer.
Pair with AI on real codebases: scoped prompts, code review loops, API integrations, and shipping small tools fast.
If you've opened ChatGPT and thought "now what?", start with a working workflow instead of another list of tips.
AIStack stands on 5 connected pillars. Prompts feed automations, automations feed no-code builds, and the right tooling holds it all together.
01
Hands-on guides for ChatGPT and Claude — projects, custom instructions, files, and reasoning modes.
When to reach for Claude's long-context analysis, when GPT's tool use wins, and how to set up a workspace that remembers your context.
02
Reusable prompt patterns that produce the same quality output every time you run them.
Role framing, few-shot examples, structured output, self-critique loops — with copy-paste templates for writing, research, code, and analysis.
03
End-to-end blueprints in Make, n8n, and Zapier that run while you're doing something else.
Inbox triage, lead enrichment, content repurposing, reporting — each with triggers, error handling, and cost notes.
04
Ship internal tools, dashboards, and client apps without writing a backend.
Airtable, Notion, Softr, Supabase and Lovable — how to model data, connect auth, and go from prototype to something people can actually use.
05
A clear read on the tools worth adopting — and the ones worth skipping.
Model comparisons, pricing math, API basics, and security guardrails so you can pick a stack without redoing it three months later.
The same five steps behind every guide on this site — from a vague idea to an automation that runs without you.
Most AI advice stops at the prompt. This workflow carries it all the way to a running system, with the testing and error handling that keeps it alive.
Write the job in one sentence: "Turn every new form submission into a qualified lead summary in Slack." Name the input, the output, and who reads it.
Most AI projects fail because the goal is "use AI". A single-sentence outcome tells you which model, which tool, and when you are done.
Draft the prompt with role, context, constraints, and an explicit output format — JSON, markdown table, or a fixed template.
Structured output is what makes a prompt automatable. Free-form text breaks the next step; a schema does not.
Run the prompt in ChatGPT or Claude with three real examples — the easy case, the messy case, and the edge case.
Debugging inside an automation is slow and expensive. Ten minutes in the chat window saves an hour in the builder.
Move it into Make, n8n, or Zapier: trigger, AI step, formatting step, destination. Add an error branch and a log row.
The error branch is not optional. Automations fail quietly, and a log row is the difference between trust and rework.
Run it live on a small volume for a week, check the log, tighten the prompt, then raise the limits.
Small volume first exposes the failure modes cheaply — token cost, rate limits, and the inputs you never imagined.
Ready-to-copy flows for Make, n8n, and Zapier: trigger, AI step, formatting, destination — with the error handling most tutorials skip.
Inbox triage, lead enrichment, content repurposing, weekly reporting, support drafting — each blueprint lists the tools, the prompt, and the monthly cost before you build it.
1
Trigger per Flow
Every blueprint starts from one clear trigger — a new row, a new email, a webhook, a schedule.
3-6
Steps per Flow
Short chains are debuggable chains. Each blueprint stays under six steps, with the AI call isolated in one node.
5h+/wk
Typical Time Saved
A single triage or reporting flow usually removes several hours of copy-paste work every week.
Why most AI automations break
They rely on free-form model output, have no error branch, and nobody notices when the flow silently stops. Every blueprint here ships with structured output, retries, and a run log so you find out before your client does.
Five non-negotiable rules for every automation you put into production.
One trigger per automation — never chain two unrelated jobs into one scenario
Force structured output (JSON or a fixed template) from every AI step
Always add an error branch that notifies you instead of failing silently
Log every run: input, output, tokens and cost, so you can audit later
Keep API keys in the platform's secret store — never inside a prompt or a note
Four prompt patterns that cover almost every job you will give ChatGPT or Claude — copy them, swap the content, keep the structure.
Pattern 1
Open with who the model is, what it is working on, and what it must not do. Then give the task and the audience.
💡 Constraints do more work than adjectives. "Under 120 words, no marketing language, cite the source line" beats "write it well".
Pattern 2
Ask for a strict format — JSON keys, a markdown table, or a template with fixed headings — and give one example of it.
💡 Any prompt you plan to automate must return structure. One example of the exact shape is worth three paragraphs of instruction.
Pattern 3
Paste two or three input/output pairs that show the style you want, then give the real input.
💡 Show, do not describe. Examples transfer tone and formatting far more reliably than instructions about tone and formatting.
Pattern 4
After the first answer, ask the model to list what is weak, unsupported, or missing — then rewrite using its own critique.
💡 Two passes beat one long prompt. Claude is especially strong at auditing its own draft when you name the criteria to check against.
Week 1
Week 2
Week 3
Week 4
"The gap is never the model.
It is the workflow nobody wrote down."
AIStack
www.aistack.pro · hello@aistack.pro
@aistack · AI guides, automations, no-code and prompts.