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?

Good point — wanting trustworthy sources is the right place to start. It makes the rest of the work faster and keeps your paper credible. AI can help you find and organize potential sources, but it won’t replace the checks you’ll need to do yourself.

Here’s a practical, step-by-step way to use AI alongside traditional checks. What you’ll need: a clear research question, a few keywords or phrases, and access to at least one reliable search tool (university library, Google Scholar, or a public research database). You’ll also want a simple checklist for evaluating sources (author, publication, date, citations, conflicts of interest).

1. Prepare your basics. Write one clear sentence that states your topic or question and list 3–6 keywords or related terms.
2. Ask AI for search ideas and types of sources. Ask it to suggest useful search terms, key journals, prominent authors, or review articles in your field—phrase it as a brainstorming step rather than a final answer.
3. Get a short candidate list. Have the AI produce a brief annotated list (3–8 items) of possible sources and why each might be relevant. Treat this as a starting list, not proof of existence.
4. Verify each item. For each suggested source, check: a) does the article or book actually exist (search title in Google Scholar or a library catalogue); b) who is the author and what are their credentials; c) where was it published (peer-reviewed journal, academic press, reputable organization); d) how recent is it and does it cite evidence. If you can, open the full text and skim the abstract, methods, and conclusion.
5. Cross-check and expand. Use the references in any good review article you find and check citation counts on Google Scholar. If the AI suggested something that can’t be found, drop it and ask the AI for alternatives.
6. Organize and cite. Keep a short note for each source: one-line summary, why it’s useful for your paper, and the full citation. That makes drafting and referencing much easier.

What to expect: AI will speed up brainstorming and summarizing, but it can also invent or misstate citations. Always confirm existence and details in a trusted database. Expect to do some manual verification — that’s normal and important.

Quick tip: start with recent review articles on your topic — they point to the most reliable primary research. Do you have a specific topic or access to a university library that I should keep in mind?

Good point — focusing on speed and clarity is exactly the right place to start when using AI for RFPs. Below I’ll walk you through a simple, practical process you can try today, written for non-technical users.

What you’ll need

• Digital copies of the RFPs (PDF or text). If you only have paper, a clear scan or photo will do.
• A simple comparison template (a spreadsheet with columns like Deadline, Mandatory Requirements, Evaluation Criteria, Budget, Questions, and Notes).
• Access to a chat-style AI helper (web or email-based works) and a short library of your standard answers or capabilities.

Step-by-step: how to do it

1. Gather and label: Put each RFP in a single folder and name files clearly (issuer + date). This saves time later.
2. Triage each RFP: Open the RFP and pull the basics into your template: submission date, mandatory must-haves, budget/format rules. You can copy-paste or type short bits into the spreadsheet.
3. Ask the AI to extract and summarize: In plain language tell the AI you want a short list of “mandatory requirements,” “key evaluation criteria,” and “important dates.” Use short chunks of text (one section at a time) rather than the whole document at once. Try three modes: quick triage (one-line summary), side-by-side compare (bulleted differences), and gap analysis (what we don’t meet).
4. Draft responses: For each mandatory item, ask the AI to suggest a concise answer using evidence from your library (examples, metrics, past projects). Keep replies short—one to three sentences—so reviewers can scan quickly.
5. Human review and red flags: Verify numbers, legal clauses, and any statements that could create obligations. Flag anything unclear to ask the issuer. Make a short list of follow-up questions to confirm before sending.

What to expect

• Biggest time savings in the triage and first-draft stages — AI helps summarize and compare fast.
• AI can miss nuance, so plan for a careful human check of compliance and contract language.
• Start with one or two RFPs so you can refine your template and prompts until the results fit your style.

Quick clarifying question: about how many RFPs do you handle a month, and do you prefer working in a spreadsheet or a Word-style document?

Quick win: in under 5 minutes, pick a short paragraph (3–5 sentences), ask an AI to make 3 literal comprehension questions and 2 inference questions, then check the answers—this shows you how useful the tool can be without any setup.

That’s a great question — AI can definitely help create reading-comprehension questions at different difficulty levels, and it’s easiest if you give it a clear purpose (age group, curriculum goal, or question count). The output is usually a strong first draft you can tweak for clarity, cultural fit, and accuracy.

1. What you’ll need
   • A short passage (or the title and topic if you want the AI to pick one).
   • A target reader (age, grade, or adult ESL level).
   • An idea of how many questions and what types (multiple-choice, short answer, vocabulary, inference).
2. How to do it — simple step-by-step
   1. Decide the difficulty bands you want (easy = literal/vocab, medium = inference/multi-step, hard = analysis/synthesis).
   2. Give the passage and tell the AI the reader level and how many questions per band you want.
   3. Ask the AI to produce questions plus brief answers or model responses and a one-sentence rubric for grading.
   4. Review quickly: check facts, ensure language is age-appropriate, and adjust any ambiguous wording.
3. What to expect
   • Fast drafts that usually cover literal and basic inferential questions well.
   • Medium and hard questions may need fine-tuning so they truly require deeper thinking or textual evidence.
   • Multiple-choice distractors sometimes need editing to avoid accidental clues or impossible options.

Simple tip: ask the AI to explain, in one sentence, why each question fits its difficulty level — that helps you learn to calibrate future requests. Would you like examples tailored for a specific grade or for adult learners?

Nice starting point — wanting a repeatable SOP library is exactly the right move for scaling a side hustle without burning out. Below I’ll give a clear do / do-not checklist, step-by-step guidance (what you’ll need, how to do it, what to expect), and a short worked example you can adapt.

• Do: Keep SOPs short, action-focused, and test them on a real run.
• Do: Store one canonical version in a simple place (a folder or notes app) and date each edit.
• Do: Include the expected time, materials, and common troubleshooting steps.
• Do-not: Overload each SOP with background or long theory—save that elsewhere.
• Do-not: Assume perfection on first draft—expect a couple of revisions after you try it.

1. What you’ll need:
   • a list of repeat tasks you do each week or month;
   • one place to keep documents (folder, notes app, or simple cloud doc);
   • a way to record a quick demonstration (voice memo or short video) if helpful.
2. How to do it (step-by-step):
   1. Pick 1 task to start — something small you do at least once a week.
   2. Run through the task slowly and jot down each real action (opens, clicks, materials, timings).
   3. Turn those notes into a short numbered SOP: purpose, scope, step-by-step actions, expected time, last-checked date.
   4. Ask an AI tool to help tighten language or create templates (e.g., short customer messages or checklists) — keep the outputs simple and edit them.
   5. Test the SOP by doing the task using only the SOP. Note missing steps and update the document.
   6. Store the final SOP in your library and add a 3–6 month review date.
3. What to expect:
   • First SOPs take time; later ones will be faster.
   • You’ll reduce guesswork and errors, and free mental space for higher-value work.

Worked example — “Shipping an order” SOP:

1. List inputs: order details, packing materials, shipping label account, customer email.
2. Step-by-step actions: print packing slip (30s), pack item and add protective material (3–5 min), weigh and print label (1–2 min), drop-off or schedule pickup (time varies).
3. Include templates: short shipping notification and tracking email (one-sentence update plus link placeholder).
4. Test: time the whole process once; note where you can batch steps (print labels for 5 orders at once).
5. Refine: add common problems (wrong address, damaged item) and quick fixes.

Quick tip: start with the task that frustrates you most — that’s where SOPs give the biggest relief. What’s one repeat task you’d like to make into an SOP first?

Good call focusing on recurring tasks — that’s where SOPs save the most time and reduce stress. I like the idea of using simple AI prompts to jumpstart a clear, consistent SOP instead of starting from a blank page.

• Do: start with the goal (why this task matters), the frequency, and the owner.
• Do: break the task into discrete, ordered steps with estimated times.
• Do: include checks, handoffs, and where to find templates or files.
• Do not: assume everyone knows implicit rules—spell out decision points and thresholds.
• Do not: overload the SOP with long paragraphs—use numbered steps and short bullet notes.

Here’s a clear, practical way to use AI to create an SOP, step-by-step:

1. What you’ll need: a short description of the task, who does it, how often, and any files or systems involved (names only, no sensitive info).
2. How to do it:
   1. Tell the AI the task goal and frequency (one-sentence goal is fine).
   2. List the team roles (owner, reviewer, approver).
   3. Ask the AI to outline the task in 6–10 numbered steps, each with a time estimate and the responsible role.
   4. Ask for common mistakes or checks to include at the end (a short checklist).
   5. Ask for a short header: purpose, scope, owner, last updated.
3. What to expect: a concise draft you can edit in 10–20 minutes—clear steps, role tags, and a brief checklist. You’ll still need to confirm system names and exact file paths.

Worked example (short, practical): create an SOP for a “Monthly Budget Review”. I won’t give a full copy/paste prompt, but here’s the structure you’d use with the AI and the expected result.

• Input structure: goal: reconcile budget vs. actual each month; frequency: monthly; owner: Finance Lead; files: Budget workbook, Expense report.
• Expected AI output: a header (purpose, owner, frequency), a 7-step numbered procedure (open files, update actuals, reconcile variances, flag issues), time estimates (30–60 minutes), role assignments for each step, and a 5-item checklist (sign-off, archive, notify stakeholders, update forecast, log exceptions).

Simple tip: keep one master SOP file per recurring process and add an “updates” line whenever you change a step—this keeps everyone aligned without rewriting the whole document.

Quick win: In under 5 minutes pick one word that sums up your brand, choose one or two colors, and ask the model for a simple geometric mark plus the word — you’ll get usable ideas fast.

Nice focus asking for example prompts and practical tips — that’s exactly what speeds up good results. Below is a gentle, step-by-step approach you can try right away and repeat until you land on a logo you like.

1. What you’ll need
   • a short brand name or initials (1–3 words)
   • 2 color choices (primary and neutral)
   • a few style words (for example: modern, minimalist, geometric, elegant)
   • a device to view results (phone or laptop) and a simple image editor if you want to crop)
2. How to do it — step by step
   1. Decide the core idea: wordmark, monogram (initials), or symbol (shape that represents your brand).
   2. Choose 2 style words (e.g., “modern” and “minimalist”) plus one composition note (e.g., “mark to left of word” or “stacked”).
   3. Pick 2 colors. Try one dark and one neutral for contrast; also ask for black-and-white versions to test clarity.
   4. Ask for several variations in one go: different marks, different placements, and a grayscale test to check legibility at small sizes.
   5. Run the generator, review 4–8 options, then request small refinements: thicker lines, simplification, or swapped color.
3. What to expect
   • Quick visual ideas that capture mood and composition; many will be strong starting points but not final files.
   • Raster images (PNGs/JPEGs) suitable for mockups. For a true, editable logo you’ll likely have a designer trace or recreate a chosen concept as a vector (SVG/AI).
   • Iterate 2–3 times: a tiny tweak (line weight, spacing, color) often makes a big difference.

Simple prompt structure you can follow in your own words: name/initials + style words + color choices + composition (mark placement) + rendering hints (flat, high contrast, no gradients) + request for black-and-white and small-size tests. Keep each instruction short and concrete.

One small tip: always view designs at phone-icon size (about 40–60 pixels) — if the mark reads clearly there, you’ve nailed legibility. Do you have a preferred color or one-line brand description I can help simplify into that structure?

Short answer: you can use AI as a fast, structured thinking partner to turn your assumptions into numbers and three clear scenarios (conservative, realistic, optimistic). AI won’t replace judgment, but it will help you list what matters, run the math, and show which assumptions change the timeline to profit the most.

What you’ll need

• Basic inputs: planned price per sale/service, expected conversion rate or sales per week, hours you’ll work per week, upfront and ongoing costs (ads, supplies, tools).
• Local/context facts: market price range, any regulatory fees, and how you’ll find customers (organic, paid ads, referrals).
• Comfort level with risk: do you want a cautious estimate or one that assumes faster growth?

How to do it — step by step

1. Write down your core assumptions (price, hours, leads per week, conversion rate, fixed & variable costs).
2. Ask an AI to build a simple cash-flow timeline: months on the x-axis and cumulative profit/loss on the y-axis. Tell it to create three scenarios (conservative/realistic/optimistic) and to show the month when cumulative profit turns positive for each.
3. Request a sensitivity check: which two assumptions change the time-to-profit the most if they vary by ±20%?
4. Run a short validation test in real life (2–6 weeks): try acquiring a small number of customers and compare actual conversion and cost-per-customer to your assumptions.
5. Refine the model with real data and re-run the scenarios. Repeat quarterly until the pattern stabilizes.

What to expect

• You’ll get a range (not a single date). The realistic scenario is usually the most useful.
• AI can highlight the key drivers (price, conversion, marketing cost) and produce simple tables or month-by-month numbers you can paste into a spreadsheet.
• Plans will change after you test: treat the first 3 months as discovery, then tighten your forecast.

Prompt help (short and flexible): tell the AI your key assumptions, ask for three scenarios, ask for the month when cumulative profit turns positive, and ask which inputs matter most. For variants, request: 1) conservative (lower sales, higher costs), 2) realistic (your best estimate), 3) optimistic (faster customer growth).

Simple tip: run a 90-day paid-or-organic test for real customer data — it cuts uncertainty faster than perfect planning. Quick question: what kind of side gig are you considering and how many hours per week can you commit?