3 prompt patterns that actually work, with copy-paste examples

Dex — Mon, 28 Sep 2026 23:18:46 +0000

Most bad AI output is a bad prompt, not a bad model. These three patterns fix the majority of it. Each one includes a template you can copy.

1. Role plus constraints

Vague prompts get vague answers. Give the model a role and hard constraints:

You are a senior Python reviewer. Review the code below.
Rules:
- List only real bugs, not style nits
- Max 5 items
- Each item: one line describing the bug, one line with the fix

The constraints do the heavy lifting. "Max 5 items" and "one line each" force density. Without them you get a polite essay.

2. Few-shot examples

Show the model what good output looks like instead of describing it. Two examples beat two paragraphs of instructions:

Convert these into calendar event titles:

Input: "Lunch with Sara tomorrow at noon"
Output: "Lunch - Sara"

Input: "Dentist appointment Friday 3pm"
Output: "Dentist"

Input: "Call the bank about the fee on Monday morning"
Output:

The model continues the pattern. This works for formatting, classification, tone matching, and extraction. Use it whenever the output has a shape you can demonstrate.

3. Ask for questions first

For anything complex, make the model interrogate you before it answers:

I want to automate my invoice reminders. Before you suggest anything,
ask me every question you need answered to give a good recommendation.

This kills the biggest failure mode: the model guessing your context and confidently solving the wrong problem. Answer its questions, then let it work.

Putting it together

A strong prompt usually combines all three: a role with constraints, one or two examples of the output shape, and a questions-first step for anything ambiguous. Try rewriting your most-used prompt with this structure and compare the outputs.

I build AI automations for businesses. Chatbots, workflow automation, custom tools that cut manual work. Engineering proof: github.com/Dextheking1. DMs open on X: @FREEAIMETHODS.

Automate a boring task for $0 with the Google AI Studio free API tier

Dex — Mon, 28 Sep 2026 16:29:04 +0000

Automate a boring task for $0 with the Google AI Studio free API tier

Google AI Studio gives you a free Gemini API key with a generous free tier. No credit card. Here is the full path from zero to a working automation.

1. Get your free API key

Go to aistudio.google.com, sign in with a Google account, and click "Get API key". Copy it. Treat it like a password: put it in an environment variable, never in your code.

export GEMINI_API_KEY="your-key-here"

2. Make your first call

No SDK needed. Plain curl works:

curl "https://generativelanguage.googleapis.com/v1beta/models/gemini-2.5-flash-lite:generateContent" \
  -H "Content-Type: application/json" \
  -H "x-goog-api-key: $GEMINI_API_KEY" \
  -d '{"contents":[{"parts":[{"text":"Summarize this in one sentence: The quick brown fox jumps over the lazy dog."}]}]}'

gemini-2.5-flash-lite is the fastest model and has the most generous free-tier quota, so it is the right default for automations.

3. Build something real: a text summarizer

Say you wake up to 40 unread newsletters and threads. This script condenses pasted text into 5 bullets:

import os, sys, json, urllib.request

API_KEY = os.environ["GEMINI_API_KEY"]
MODEL = "gemini-2.5-flash-lite"
URL = f"https://generativelanguage.googleapis.com/v1beta/models/{MODEL}:generateContent"

def summarize(text):
    body = json.dumps({
        "contents": [{"parts": [{"text":
            "Summarize the following in exactly 5 short bullets:\n\n" + text}]}]
    }).encode()
    req = urllib.request.Request(
        URL, data=body,
        headers={"Content-Type": "application/json", "x-goog-api-key": API_KEY})
    with urllib.request.urlopen(req) as res:
        data = json.load(res)
    return data["candidates"][0]["content"]["parts"][0]["text"]

if __name__ == "__main__":
    print(summarize(sys.stdin.read()))

Usage:

cat long-article.txt | python summarize.py

Zero dependencies beyond the standard library. Run it on a schedule with cron and you have a free daily digest.

4. Know the limits

The free tier has rate limits (requests per minute and per day). They change, so check your current quota at aistudio.google.com under usage. If you hit a 429, slow down and retry later.

One real caveat: do not send private or customer data through the free tier. Keep it to public or personal content.

Where this goes

Once you see what one script can do, the next step is wiring it into a real workflow: inbox triage, lead follow-ups, report generation. That is the part I do for businesses.

I build AI automations for businesses. Chatbots, workflow automation, custom tools that cut manual work. Engineering proof: github.com/Dextheking1. DMs open on X: @FREEAIMETHODS.

DEV Community: Dex

3 prompt patterns that actually work, with copy-paste examples

1. Role plus constraints

2. Few-shot examples

3. Ask for questions first

Putting it together

Automate a boring task for $0 with the Google AI Studio free API tier

Automate a boring task for $0 with the Google AI Studio free API tier

1. Get your free API key

2. Make your first call

3. Build something real: a text summarizer

4. Know the limits

Where this goes