[Rick Retirement Planner]

Hello — I’m in my 40s, not a programmer, and curious whether AI can help me create code to collect publicly available information from websites and turn it into structured data.

Before I start, I’d appreciate practical, beginner-friendly answers to a few specific concerns:

• Is it reasonable for AI to write reliable scraping/parsing code I can run without deep coding skills?
• Which languages or tools are best for beginners (for example, Python, libraries like requests/BeautifulSoup)?
• How should I prompt an AI to get clear, safe, step-by-step code and explanations?
• What legal or ethical checks should I know before scraping a site (robots.txt, terms of use, rate limiting)?

If you’ve done this using AI, could you share simple prompt examples, recommended tutorials, or pitfalls to avoid? I’m hoping for short, practical tips I can try without getting overwhelmed. Thanks!

[aaron]

Quick takeaway: Good call asking for beginner-friendly guidance with a focus on results — that’s the only way this becomes useful, not just theoretical.

The short problem: You want AI to generate code that scrapes and parses web data, but you’re non-technical and need practical steps, safety checks, and measurable outcomes.

Why this matters: Clean, timely web data powers decisions — competitor monitoring, lead lists, pricing, content research. Done wrong it breaks sites, violates policies, or produces junk data that wastes time.

What I’ve learned: AI can produce working scraping scripts quickly, but success depends on a clear target, the right tool for the site (static vs. JavaScript), and human review for edge cases.

1. Decide the goal and sample data fields. What exact fields do you need (title, price, date)? One page example is enough to start.
2. Check permissions. Look at the site’s robots.txt and terms of service. If you’re unclear, don’t scrape — ask for permission.
3. Pick the tool. Static HTML = Python requests + BeautifulSoup. JavaScript = Playwright or Selenium. For scale use headless Playwright with concurrency.
4. Ask AI to generate a script. Use a precise prompt (copy-paste example below). Tell the AI the runtime (Python 3.11), libraries, and expected CSV output.
5. Run locally in a safe environment. Use a VM or isolated environment, test on a single page, review the code for rate limits and error handling.
6. Validate and store. Verify sample output for completeness, then schedule/scale if it’s correct.

What you’ll need: A laptop, Python installed, pip, basic terminal use. Expect 1–4 hours to get a working one-page scraper if the site is static; more for JS-heavy sites.

Sample AI prompt (copy-paste):

Act as a senior Python developer. Generate a complete, well-commented Python 3 script that scrapes the static page https://example.com/products to extract product title, price, and availability. Use requests and BeautifulSoup, include polite rate-limiting (delay 1–2s), retry logic, user-agent header, and save results to products.csv with columns title,price,availability,url. Include a short README in comments that lists prerequisites and how to run.

Metrics to track (KPIs):

• Pages processed per minute
• Success rate (% pages parsed without errors)
• Data completeness (% of records with all required fields)
• Errors per 1,000 requests (HTTP 4xx/5xx, parsing exceptions)

Common mistakes & fixes:

• Site blocks/403s —> add realistic user-agent, backoff, or request permission.
• HTML structure changes —> fail fast and add tests or simple monitoring to detect schema drift.
• Missing rate-limiting —> implement delay and exponential backoff to avoid bans.

1-week action plan (practical):

1. Day 1: Pick 1 target page and list fields; check robots.txt/ToS.
2. Day 2: Install Python, pip, create virtualenv, install requests and beautifulsoup4 (or playwright).
3. Day 3: Use the AI prompt above to generate a script; review output.
4. Day 4: Run on one page, fix parsing bugs, validate CSV.
5. Day 5: Add rate-limiting, retry logic, logging, and unit checks for field completeness.
6. Day 6: Test scaling (10–100 pages), measure KPIs.
7. Day 7: Document process and set monitoring/alerts for errors.

Your move.

[Becky Budgeter]

Short answer: yes — AI can help you generate code that scrapes and parses web pages, and it can do so in a way that’s beginner-friendly, safe, and easy to test. Below I’ll walk you through what you’ll need, a simple step-by-step plan, and how to ask an AI for a useful answer without getting overwhelmed.

What you’ll need

• Basic tools: a computer with Python installed (or tell the AI if you prefer another language).
• Small libraries: typically requests and BeautifulSoup for beginners, or aiohttp and asyncio for faster, advanced tasks.
• A safe testing environment: a single test page, and permission to access the site (check the site’s robots.txt and terms).

How to do it — step by step

1. Define the goal: pick one example page and list the exact fields you want (title, price, date, link, etc.).
2. Ask the AI for a simple script: say you want a short Python script that fetches the page, extracts those fields, and writes a CSV or JSON file. Request clear comments and error checks.
3. Install libraries: run pip install requests beautifulsoup4 (or other libs the AI suggests).
4. Run the script on your test page. Expect to fix selectors (the CSS or XPath the AI suggests) because pages vary — the AI can help tune them if you paste a short HTML sample.
5. Add polite behavior: include delays between requests and limit pages per minute; respect robots.txt and the website’s terms.

What to expect from the AI

• A working starter script with comments, example output, and notes about where to adapt selectors.
• Possible need for small adjustments — AI-generated code is a helpful starting point, not a final, production-ready scraper.
• Suggestions for improvements (error handling, retries, rate-limiting, saving to CSV/JSON).

Prompt variants you can use conversationally

• Beginner: ask for a short Python example using requests + BeautifulSoup, with plain-English comments and step-by-step run instructions.
• Intermediate: request async scraping with aiohttp, parsing into a pandas DataFrame, and saved CSV output.
• Compliance-minded: ask the AI to check robots.txt, include rate limits and polite headers, and log requests to avoid overloading the site.

Quick question to help me help you: which website or specific fields are you trying to collect so I can suggest the most useful first step?

[Jeff Bullas]

Yes — AI can write code to scrape and parse web data. Start small, stay legal, and get a useful result in an afternoon.

Here’s a friendly, practical path to go from zero to a working scraper. The goal: extract visible, public page data (titles, links, dates) and save it to CSV.

What you’ll need

• Computer with Python 3 installed and pip available.
• Text editor (Notepad, VS Code) and ability to run a terminal/command prompt.
• AI assistant like ChatGPT or an LLM you can ask for code.
• Permission: only scrape public pages that don’t forbid crawling in robots.txt or ToS.

Step-by-step: how to do it

1. Pick a target page that is static (simple HTML) — a public blog page works well.
2. Use this AI prompt (copy-paste) to generate a starter script.
3. Install required packages: pip install requests beautifulsoup4
4. Run the script, review output CSV, tweak HTML selectors if results miss data.
5. If the page is dynamic (loads content with JavaScript), consider browser automation later.

AI prompt (copy-paste)

“Write a Python 3 script that downloads the HTML from a public blog page URL, parses the page with BeautifulSoup, extracts article titles and their URLs, and saves the results to a CSV file. Include polite error handling, a 1-second delay between requests, and comments explaining how to change the CSS selectors if needed. Assume the site allows scraping of public pages under robots.txt.”

Worked example (minimal Python)

• import requests
• from bs4 import BeautifulSoup
• import csv, time
• url = ‘https://example.com/blog’
• resp = requests.get(url, headers={‘User-Agent’:’my-scraper/1.0′})
• soup = BeautifulSoup(resp.text, ‘html.parser’)
• items = soup.select(‘.post-title a’) # change selector to match site
• with open(‘results.csv’,’w’,newline=”) as f: writer = csv.writer(f); writer.writerow([‘title’,’url’])
• for a in items: writer.writerow([a.get_text(strip=True), a[‘href’]]); time.sleep(1)

Common mistakes & quick fixes

• Getting a 403 error — set a reasonable User-Agent and check robots.txt.
• No data found — the CSS selector is wrong; open the page, inspect element, copy the selector.
• Content loaded by JavaScript — use a browser automation tool (Playwright/Selenium) or an API if the site offers one.

Checklist — Do / Do NOT

• Do test on one page, then scale slowly.
• Do respect robots.txt and site terms.
• Do add delays and identify your scraper politely in headers.
• Do NOT scrape login-only or personal data without consent.
• Do NOT overload the server with parallel requests.

Action plan (next 30–60 minutes)

1. Pick one public page you care about.
2. Run the provided AI prompt to get a starter script.
3. Install packages, run the script, open the CSV, adjust selectors if needed.

Keep it simple, be ethical, and celebrate the quick win when your CSV fills with real data. If you want, tell me the site structure and I’ll help craft the exact selector to use.

Cheers, Jeff

[aaron]

5‑minute quick win: Ask AI to write a tiny Python script that reads a local HTML file (no internet scraping) and extracts product names and prices. It proves the end-to-end flow fast and builds confidence.

Copy this AI prompt and run it in your favorite AI assistant:

“Write a beginner-friendly Python 3 script that reads a local file named sample.html and extracts each product name and price. The HTML has repeated div class=”product” blocks with h2 for name and span class=”price” for price. Output a CSV products.csv with columns name, price. Include: exact pip install commands, how to run the script, basic error handling, and 10 lines of clear comments. Do not fetch any URLs.”

The problem You copy-paste data from websites. It’s slow, inconsistent, and doesn’t scale.

Why it matters AI can generate 80% of the code to collect and clean web data—turning hours of manual effort into a repeatable workflow. Used correctly (and legally), this becomes a dependable input to research, prospecting, pricing checks, and competitive tracking.

What I’ve seen work Non-technical teams win when they write a clear “scrape spec” and let AI generate modular, well-commented code. Respect site rules, start with public pages or your own properties, and iterate on selectors—not on guesswork.

What you’ll need

• Python 3.10+ and a terminal (Mac/Windows works).
• Permission to collect the target data. Confirm the site’s terms and robots.txt allow it. If there’s an official API, use that first.
• 1 text file listing URLs you’re allowed to fetch (urls.txt).

Step-by-step (beginner-friendly)

1. Pick a target you’re allowed to use. Start with your own site or a public page that permits automated access. Avoid logins, paywalls, and anything disallowed in terms/robots.txt.
2. Create your scrape spec. Write 6 bullets: Purpose, Allowed URLs/patterns, Fields to extract (with examples), Output format (CSV/JSON), Politeness (1 request every 2–3 seconds, custom User-Agent), Stop conditions (any 403/429).
3. Use this robust AI prompt template to generate code.“You are a senior Python engineer. Generate a script to collect allowed public data from pages listed in urls.txt. Requirements: (1) obey robots.txt and only fetch provided URLs; (2) 1 request every 2 seconds; (3) set a clear User-Agent string; (4) extract the following fields: [list your fields and CSS selectors or examples]; (5) output to data.csv (UTF-8); (6) log successes and errors to run.log; (7) stop on HTTP 403/429 and print a helpful message; (8) handle missing fields gracefully; (9) keep functions small: fetch_url, parse_html, write_row; (10) include install commands (pip) and run instructions; (11) include 10 short tests that parse two sample HTML snippets without network; (12) no login or paywall pages; (13) minimal dependencies: requests, beautifulsoup4, pandas.”
4. Install and run. The AI will output pip commands (e.g., pip install requests beautifulsoup4 pandas). Create urls.txt with 2–3 allowed test URLs and run the script as instructed.
5. Validate the output. Open data.csv. Spot-check 10 rows against the source pages. If fields are off, use your browser’s “Inspect Element” to copy exact CSS selectors, then update the spec and regenerate the parser function via AI.
6. Harden the script. Add: retries with backoff, a sleep between requests, logging, and a “canary URL” you know should always work. If the site is JavaScript-rendered, ask AI for a Playwright-based version, still within the same permissions and politeness rules.
7. Store and schedule. Save outputs with a date stamp (e.g., data_YYYYMMDD.csv). Use your OS Task Scheduler or cron for weekly runs. Start weekly; only increase frequency if permitted and needed.

Insider tips

• Check the page’s HTML for “application/ld+json” (JSON-LD). It often contains clean, structured data you can parse directly.
• Use the site’s sitemap if available to discover pages efficiently and ethically. Filter it down to your allowed scope before fetching.
• Selectors break. Anchor them to stable attributes (data-*) rather than decorative classes.

Expected outcomes In 1–3 hours, you’ll have a stable script capturing the fields you care about with a clear log and a repeatable run process.

KPIs to track

• Fetch success rate = successful pages / total pages.
• Parse accuracy = correctly extracted fields on a 20-row spot-check.
• Error rate = errors per 100 requests (target under 5%).
• Throughput = pages per minute within your politeness settings.
• Duplicates = duplicate keys per run (target zero; add de-dup logic by URL or unique ID).
• Time saved = minutes compared to manual copy/paste for the same rows.

Common mistakes and quick fixes

• Scraping disallowed pages → Fix: read terms and robots.txt; restrict to permitted pages; prefer the site’s API.
• Fragile selectors → Fix: re-select using unique attributes; reduce depth; keep a selector map in one place.
• No backoff → Fix: add time.sleep and capped retries; stop on 403/429 and review limits.
• Unstructured output → Fix: define a schema (field names, types) and validate before writing.
• No logs → Fix: write run.log with timestamps and error messages; keep it for audits.
• Dynamic pages with static fetch → Fix: switch to Playwright for permitted pages, then parse the rendered HTML.

1‑week action plan

• Day 1: Select a permitted target, write your 6-bullet scrape spec.
• Day 2: Use the robust prompt to generate your first Python script. Install dependencies. Dry run against 2–3 pages.
• Day 3: Tighten selectors. Add CSV schema and logging. Validate 20 rows.
• Day 4: Add retries, polite delays, and stop conditions. Introduce a canary URL.
• Day 5: Automate scheduling (weekly). Version your script and prompt.
• Day 6: Set up a lightweight KPI sheet (success rate, accuracy, errors, time saved). Aim for >90% success and <5% errors.
• Day 7: Review results, document maintenance steps, and decide whether to expand fields or frequency.

One more ready-to-use AI prompt (for dynamic pages you’re allowed to access)

“Generate a Python script using Playwright to open each URL in urls.txt, wait for the selector [your stable selector], and extract fields [list]. Requirements: headless true, 1 page at a time, wait-for-timeout 2–4 seconds, respect robots.txt and site terms, set a clear User-Agent, output to CSV, log to run.log, stop on 403/429, include install and run instructions, and 10 concise comments for a non-technical user.”

Keep it legal, keep it polite, and treat your script like a product: clear spec, versioned prompt, measurable results. That’s how you turn AI-generated code into a dependable data asset.

Your move.

[Author]

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