How One Learner Manually Built 1,100 Flashcards

One learner spent 60 hours turning a single textbook into more than 1,100 flashcards by hand. The work was mechanical and slow: read a section, pull out the concepts worth testing, write a front and a back for each one, then keep the growing pile organized. That 60-hour figure is the baseline any ai flashcard generator gets measured against, and it explains why the workflow below is worth learning.

What You Need to Generate Flashcards With AI

The list is short: a free OpenAI account, Microsoft Excel or a similar spreadsheet, and an electronic study resource you can copy text out of. ChatGPT’s free version does the generating, so there is no premium tier to buy, no plugin to install, and no advanced setting to hunt down. Most people already have two of the three.

Why Cloze-Deletion Cards Speed Up Recall

A cloze-deletion card is a sentence with one key term blanked out, and your job is to supply the missing piece from memory. That takes far less effort per card than reading a full question and composing a paragraph-length answer, so you cover more material in a single sitting. The blank forces recall instead of recognition.

Writing a ChatGPT Prompt That Produces Quality Flashcards

Card quality tracks prompt quality almost one for one. A loose prompt gives you cards you skim past without really answering, while a prompt that spells out what a good card looks like in your subject gives you cards worth the repetitions. Spend your effort here, before a single card exists.

Prompt Structure: Four Essential Elements

  1. Context statement: Name the subject and the audience. “Generate cloze-deletion flashcards for first-year chemistry students studying thermodynamics.”

  2. Formatting instructions: Ask for a single-column table with the card text in column A, so the output drops straight into a spreadsheet without reshaping.

  3. Reference criteria: Describe what good looks like. “Each card tests one concept only. Avoid compound questions that require multiple answers.”

  4. Multiple examples: Show 2-3 sample cards in the exact format you want. Examples move output quality more than any other part of the prompt, so include plenty of them.

Here’s a practical template:

Context: You are a flashcard generator for medical students studying pharmacology.

Format: Output cards in a single-column table, each card on one row.

Quality criteria:
- Each card tests one drug interaction or mechanism only
- Use precise medical language; avoid vague terms
- Each card must be understandable without additional context

Example cards:
[Drug A _____ inhibits CYP3A4, reducing metabolism of statins]
[Mechanism: Grapefruit juice blocks _____ in gut wall, increasing absorption]

With the prompt settled, paste your material in one section at a time. ChatGPT returns cards built to the spec you just wrote, which leaves you far less to clean up afterward.

Prompt Length and Best Practices

The free tier accepts prompts of roughly 4,000 words, but output quality holds up best around 400. The extra room tempts you to over-explain, and a tight prompt reliably beats a sprawling one.

Feed the textbook in sections rather than dropping an entire chapter into one message, since a narrower slice keeps the cards consistent. State the subject explicitly inside each card as well. A card you meet again three weeks from now has to make sense on its own, with no memory of which chapter it came from.

Formatting and Exporting Your Flashcards to Excel

Paste the generated cards into Excel as a single-column table, one card per row, and the whole set stays workable. Add a second column for tags grouped by chapter, unit, or topic: a 10-chapter textbook becomes Ch1 through Ch10, and those tags turn into searchable categories once the cards reach Anki.

When every card carries a tag, save the file as CSV UTF-8. That is the format Anki expects, and skipping it is the most common reason an import fails on the first try.

Importing Cloze Cards Into Anki

Open the import screen in Anki and select your CSV file. Two settings decide whether the deck comes out usable, and Anki asks for both before the import completes.

  1. Note type: Select “Cloze”, sometimes labeled “cloze deletion” in older versions. This tells Anki the cards use fill-in-the-blank format.

  2. Field mapping: Point column A at the card text field and your tag column at the tags field, so each card lands with its chapter attached.

Anki checks for duplicates as it imports and skips any card it already has, which means a second pass after adding rows will not clutter the deck. Click import, and the spreadsheet becomes a deck queued for spaced-repetition review.

Getting the Same Cards Without the File Shuffle

The workflow holds up, but it stays a chain of tools: chat window, spreadsheet, tag column, CSV export, note type, field mapping. Forget the UTF-8 save or pick the wrong note type and you are back at the import dialog working out which step broke. Every new chapter restarts the loop, and the file management slowly eats the time you meant to spend studying.

Picture the same chapter ending differently. You write the notes, cards appear from them, and a review queue is waiting the next morning. No file conversions in between.

Fluxo is built around that shape. You keep rich-text notes in spaces and topics, and it generates flashcards, quizzes, and summaries from what you actually wrote, then schedules spaced-repetition review that resurfaces the material over time. A companion mascot, streaks, and gamified sessions keep the daily queue from turning into a chore, and it suggests topics worth exploring next when you finish one.

What it deliberately does not do is write the material for you. There are no ready-made courses, no shared deck library, and no AI drafting your notes from scratch, because the act of writing them is where most of the learning happens.