LLM-powered Anki card generation

Anki is a great tool. "Its what the Jeopardy champions use," I say to people. They roll their eyes harder.

The Problem

When I want to learn a new subject I check for existing decks on AnkiWeb, and often I'm surprised at what I can find. On the other hand, making your own Anki cards is a great way to learn in itself, but it can be kind of tedious. If only there was a way to accelerate the process of generating flashcards on a specific subject, while also including more questions and context than one person could .... oh wait

I realized that this was a perfect LLM-shaped problem. Text-generating language models are also very good at generating structured output, like CSV or JSON, which are used as data-interchange formats. They're also at good at providing varying versions of the same information, which is great for reinforcing understanding.

The following is a really simple "no-code" approach which will use the Anki desktop application to author the cards.

Two stage process

First, generate a list of high-level topics within a subject by seeding a prompt with a few ideas and some buzzwords 😄

Start by identifying high-level topics within the subject of Data Visualization. Consider the technologies, techniques, history, relevance to industries and especially the technical implementation trends over recent years.

You'll get a fire-hose of bullet points organized into topics

Expand upon the 'Foundations' topics by providing as many as possible flashcard questions, formatted as the Anki CSV format:

Here is where you start to feel the incredible time-savings:

Front,Back
"What is Edward Tufte best known for in the field of data visualization?","He is known for promoting minimalism and data integrity in visual design, including concepts like the data-ink ratio and avoiding chartjunk."
"What is the 'data-ink ratio' as proposed by Edward Tufte?","It is the proportion of a graphic's ink devoted to the non-redundant display of data information; high data-ink ratios are preferred."
"What does Tufte mean by 'chartjunk'?","Non-essential or distracting visual elements that do not improve understanding of the data and should be avoided."
"What principle suggests that simpler visuals are more effective for data comprehension?","The principle of minimalism—avoid unnecessary elements and focus on clarity."
"What are the Gestalt principles and why are they relevant to data visualization?","They are psychological principles that describe how humans perceive visual groups and structures, aiding in organizing visual data."
"Which Gestalt principle refers to grouping elements based on proximity?","The Principle of Proximity—items close to each other are perceived as related."
"What Gestalt principle is used when similar color or shape implies grouping?","The Principle of Similarity—visual elements that look alike are perceived as part of the same group."
"How does the Principle of Continuity apply to line charts?","It states that people tend to perceive continuous lines rather than disjointed segments, making line charts effective for trends."
"What is 'pre-attentive processing' in the context of visual perception?","It is the subconscious accumulation of information from the visual field; it helps viewers instantly detect differences in position, color, shape, etc."
"Why is color not always the best channel for encoding quantitative data?","Because human perception of color intensity is not as accurate as position or length; color is more effective for categorical differences."
"What is a visual encoding channel in data visualization?","It is a visual attribute (like position, size, or color) used to represent data variables in a chart."
"Which visual encoding channel is most effective for encoding quantitative values?","Position along a common scale (e.g., x/y axes) is the most accurate channel for quantitative comparisons."
"How is size typically used in data visualizations?","Size, such as the area of circles in a bubble chart, is used to encode a quantitative variable, but it’s less precise than position."
"When is color most appropriate in data visualizations?","When encoding categorical variables or highlighting specific data points."
"What does 'shape' encode best in a chart?","Shape is best for distinguishing categorical data, especially in scatter plots."
"Which type of chart is best for comparing values across discrete categories?","Bar chart—easy to compare length and position for categories."
"When should you use a line chart instead of a bar chart?","Use a line chart to show trends or changes over continuous time or ordered intervals."
"What is the primary use case for a scatter plot?","To show relationships or correlations between two numerical variables."
"When is a pie chart considered ineffective?","When comparing more than 3–4 categories or when exact comparisons are needed, as humans poorly judge angles and area."
"What type of chart is ideal for showing part-to-whole relationships?","Pie chart or stacked bar chart, but use sparingly with few categories."
"What is a histogram and when should you use one?","A histogram displays the distribution of a single quantitative variable by grouping it into bins."
"Why might you choose a boxplot over a histogram?","Boxplots summarize distribution with median, quartiles, and outliers, which is more compact and easier for comparison across groups."
"What chart type best shows multivariate data in two dimensions?","Scatter plot with additional encodings like color or size, or a bubble chart."
"What chart should you use to show network relationships between entities?","A node-link graph (force-directed or hierarchical) is commonly used."
"How does overplotting affect scatterplots and how can it be reduced?","Overplotting obscures data points; it can be reduced using transparency, jitter, or hexbin aggregation."

Then you can move right through the sets of sub-topics and request a new set of questions for each. Breaking things into chunks ensures that you get a "deep dive", or at least a few questions, per sub-topic.

Generate another series of as many questions as possible focused on real-time visualization, time-series visualization and handling data streams for visualization

Front,Back
"What is real-time data visualization?","Real-time visualization involves displaying data as it is generated or received, with minimal latency between data arrival and its visual representation."
"What are common sources of real-time data streams?","Sources include IoT sensors, financial markets, telemetry, web server logs, and user interaction events."
"What is time-series data?","Time-series data consists of data points indexed in time order, typically spaced at uniform intervals."
"What visualization types are best for real-time or time-series data?","Line charts, area charts, horizon charts, and strip plots are common for visualizing continuous changes over time."
"What is the main challenge of real-time visualization with large data streams?","Maintaining performance and responsiveness while rendering rapidly updating data."
"What is a sliding window in time-series visualization?","A technique where only the most recent N data points are displayed, allowing old data to 'slide' off the screen."
"Why are line charts commonly used for time-series data?","They emphasize trends and patterns over time by connecting data points with lines."
"What is downsampling in the context of time-series visualization?","Reducing the number of data points by summarizing or filtering to preserve overall shape while improving performance."
"What is data buffering in a live visualization system?","Temporarily storing incoming data before it's rendered, to manage latency and update frequency."
"How can WebSockets be used in real-time dashboards?","WebSockets enable bidirectional, low-latency communication between server and client, ideal for streaming updates."
"What is the role of backpressure in streaming visualization pipelines?","Backpressure is a mechanism to prevent data producers from overwhelming consumers when visual updates can't keep up."
"What JavaScript libraries support real-time charts in the browser?","Examples include Plotly.js, uPlot, Smoothie Charts, D3.js, and ECharts."
"What techniques improve performance in real-time visualization?","Techniques include throttling redraws, using WebGL rendering, aggregating data, and limiting DOM updates."
"What is throttling vs. debouncing in real-time visual updates?","Throttling limits update rate to a maximum frequency, while debouncing delays updates until input stops changing."
"What is a ring buffer and how is it used in streaming visualization?","A ring buffer is a fixed-size circular queue used to store the latest N data points efficiently."
"What does 'streaming mode' mean in Plotly or other charting libraries?","Streaming mode refers to incremental updates to the chart without full re-rendering, optimized for speed."
"Why is data aggregation important in real-time dashboards?","It reduces noise and improves readability by summarizing multiple data points into meaningful segments (e.g., min, max, avg)."
"What visualization component handles dynamic resizing and rescaling of axes in time-series charts?","Auto-scaling or adaptive axes, which adjust axis ranges to fit the incoming data in real-time."
"What are horizon charts and when are they useful?","Horizon charts use overlapping colored bands to show time-series trends compactly, useful for limited screen space."
"What is the trade-off between high-frequency updates and rendering performance?","Frequent updates improve responsiveness but can cause UI lag or dropped frames if not optimized."
"How can real-time visualizations be made responsive across devices?","Use responsive layouts, dynamic sizing, and libraries that support high-DPI and flexible rendering (e.g., SVG, Canvas, WebGL)."
"What is time alignment in the context of multi-series time charts?","Ensuring that data from different sources is synchronized on a common time axis for accurate comparison."
"How do you visualize categorical events over time?","Use event timelines, lollipop charts, or stacked bars to represent discrete events on a time axis."
"What is the purpose of zoom and pan in time-series dashboards?","They allow users to explore data at different resolutions or focus on specific periods of interest."
"How is stream processing related to visualization?","Stream processing frameworks (e.g., Kafka, Apache Flink) can prepare or transform data before it reaches the visualization layer."
"What database types are optimized for time-series data?","Time-series databases like InfluxDB, TimescaleDB, and Prometheus offer optimized querying and storage for time-indexed data."

How to Import This Into Anki

1. Copy the content above into a text file. ( Remove the 'Front, Back' header )
2. Save it as <yourfile>.csv
3. Open Anki, create a new empty Deck if you're starting something new.
4. go to File > Import.
5. Select <yourfile>.csv and ensure comma is chosen as the separator. Also check that you're importing to the right deck.
6. Anki will interpret your two values as 'Front' and 'Back'.
7. 'Import' and you're ready to study

Sync your decks to AnkiWeb and then back down to your mobile device.

These cards are the most simple type to create, but there are ways to get more complex. There are several good libraries out there in addition to the officially-supported Python interface. If I ever wanted to completely automate this it would be possible, but honestly I only have so much time to study flashcards in a given week.

Takeaways:

• You can get great structured output from LLMs. You can even provide your own schema and ask for responses to follow it. Generating JSON is a breeze
• Use a multi-prompt process to outline a subject at a high-level, then ask for more detail for each sub-section in subsequent prompts. "Generate a syllabus for a beginner Python course... Now expand on the first section in detail"