Using Generative AI to Help Build Anki Flashcards to Study for the RHCSA Exam

Executive Summary

Passing the RHCSA exam requires the complete memorization of many GNU/Linux commands, a goal best achieved through a dual strategy of practiced repetition. This video and article advocate for a method that combines hands-on lab practice, which builds muscle memory, with the use of Anki, a free and open-source flashcard application. Anki solves the primary challenge of the "forgetting curve" by implementing an intelligent, spaced-repetition algorithm. This system schedules card reviews at the optimal moment just before information is forgotten, making study far more efficient than traditional methods. The workflow detailed here is to perform a practical lab exercise and then immediately reinforce that knowledge by studying the corresponding Anki deck, bridging the gap between practical application and active mental retrieval.

A core component of this strategy is the use of generative AI, such as Google's Gemini, to create hyper-efficient flashcard content. By giving the AI a specific prompt to make answers "succinct and straight to the point," the author was able to generate a deck that strips away all explanatory text, leaving only the precise command syntax required for the exam. This video provides several examples of this, particularly for the useradd command, demonstrating how even complex, multi-option commands can be reduced to their essential syntax. This combination of hands-on labs and AI-generated, efficient Anki cards provides a comprehensive and modern approach to mastering the material.

Keywords: RHCSA, Anki, Generative AI, Google Gemini, Spaced Repetition, Flashcards, GNU/Linux, Exam Preparation, Study Strategy, Memorization, Active Recall, Forgetting Curve, useradd, LVM, AI-assisted learning, succinct flashcards, technical training

Glossary
+--Acronyms & Abbreviations
|  +--FOSS: Free and Open-Source Software
|  +--iOS: Apple's mobile operating system
|  +--LVM: Logical Volume Management
|  +--RHCSA: Red Hat Certified System Administrator
|  L--UID: User ID (User Identifier)
+--Commands (GNU/Linux)
|  +--lvcreate: Command to create a Logical Volume.
|  +--pvcreate: Command to create a Physical Volume.
|  +--useradd: Command to create a new user account.
|  L--vgcreate: Command to create a Volume Group.
+--Concepts (Learning)
|  +--Active Recall: Method of retrieving information from memory.
|  +--Flashcards: Study aids with information on them.
|  +--Forgetting Curve: Model for the decay of memory over time.
|  L--Spaced Repetition: Learning technique using increasing review intervals.
L--Software & Technologies
   +--Android: Google's mobile operating system.
   +--Anki: Spaced repetition flashcard software (from Japanese "memorization").
   +--Generative AI: Artificial Intelligence that creates new content.
   +--GNU/Linux: A family of open-source operating systems.
   +--Google Gemini: A specific generative AI model.
   +--MacOS: Apple's desktop operating system.
   L--SuperMemo SM-2: A specific spaced repetition algorithm.

Introduction

Passing the RHCSA Exam with a perfect score means that you basically have to have all of the major GNU/Linux commands and their correct syntax fully memorized. There is only one way to achieve this: practiced repetition. This repetition can come in two forms: practicing running the commands over and over again in a lab environment, and pounding the commands into your head over and over again using flashcards. In my RHCSA preparation, I'm going to do both. I've set up a Rocky Linux lab environment on Linode that I use to practice on a daily basis. I've also set up a RHCSA GNU/Linux flashcard set on ankiuser.net. Anki is a free and open-source software (FOSS) application for creating flashcards. You can run it on multiple platforms including GNU/Linux, Windows, MacOS, iOS, and Android.

Before I created my first flashcard, I did a lot of careful planning. I wanted the flashcard answer to be succinct and straight to the point. The shorter the answer, the more easily I'll be able to mentally retrieve the information during exam time. So I explicitly told my chosen generative AI, Google's Gemini, to make the answers 100% accurate, short and straight to the point.

The History of Anki

The Anki application was created by an Australian programmer named Damien Elmes. The first public release dates back to October 5, 2006. Elmes originally developed the software to assist him with his own studies, specifically for learning Japanese. The name "Anki" itself is a Japanese word (暗記) that directly translates to "memorization", perfectly reflecting the program's core purpose. Anki's effectiveness is not based on simple repetition but on a cognitive science technique known as spaced repetition. This method is powered by an algorithm, which was based on the SuperMemo SM-2 algorithm developed in the late 1980s. Anki uses its algorithm to predict the optimal time to present a flashcard to the user, ideally just before they are about to forget it, which dramatically increases memory retention and learning efficiency.

The Problem That Anki Solves

The primary challenge in mastering a large body of technical information, such as the entire RHCSA curriculum, is human memory itself. Psychologists refer to this as the "forgetting curve," a concept that illustrates how information is lost over time when we make no attempt to retain it. Traditional study methods, like re-reading a textbook or cramming with a massive stack of physical flashcards, are highly inefficient. They fail to address this natural decay of memory in a systematic way. A student might waste hours reviewing material they already know well, or wait too long to review a difficult command, forcing them to relearn it from scratch.

Anki solves this logistical problem by automating the process of spaced repetition. The software tracks the user's recall performance on every single card. Based on whether the user marks a card as easy, good, or hard, Anki's algorithm calculates the precise optimal day to show that card again. This intelligent scheduling ensures that difficult concepts are reviewed more frequently, while mastered concepts are scheduled for review further in the future. This transforms studying from a guessing game into a highly efficient, targeted process that maximizes long term retention.

How to Use Anki Effectively Along with an RHCSA Lab Practice Routine

To use Anki effectively for the RHCSA, it must be paired directly with a consistent lab practice routine. The two methods should not be treated as separate tasks; they must reinforce each other. A highly effective workflow is to first perform a hands-on lab exercise. For example, I will complete a lab focused on managing LVM (Logical Volume Management). This hands-on practice exposes me to the commands, their syntax, and the problems they solve in a real-world context. Immediately following the lab, I then study the specific Anki deck related to LVM. This tests my ability to actively recall the exact commands I just used, such as pvcreate, vgcreate, and lvcreate.

This dual approach bridges the gap between understanding and memorization. The lab environment builds practical muscle memory and contextual understanding. Anki, on the other hand, isolates the act of pure mental retrieval, which is critical for the high-pressure exam environment. Using Anki before a lab session can also serve as an excellent warm-up, priming my brain with the specific tools I am about to use. By combining practical application with algorithm-driven spaced repetition, I am training my brain not just to recognize the commands, but to produce them accurately on demand.

Examples of Flashcards That I Created Using Anki

To create efficient flashcards, I gave Google's Gemini a simple prompt. After providing a verbose answer, I asked: "Give me a much more efficient flashcard back." This process strips away all explanatory text, leaving only the direct answers needed for recall. The useradd command provides a perfect set of examples for this method. Instead of a long paragraph explaining all the command's options, the flashcards isolate each specific, testable task.

How do you add a user jdoe and add them to the supplementary (secondary) groups wheel and audio?

useradd -G wheel,audio jdoe

How do you add a user jdoe and set their default shell to /bin/bash?

useradd -s /bin/bash jdoe

How do you add a user jdoe with a specific User ID (UID) of 1050?

useradd -u 1050 jdoe

How do you add jdoe, set their primary group to users, add to supplementary groups wheel and dev, and set the shell to /bin/bash?

useradd -g users -G wheel,dev -s /bin/bash jdoe

How do you add jdoe with the comment "John Doe", a home directory of /app/johndoe, and also add them to the wheel group?

useradd -c "John Doe" -d /app/johndoe -G wheel jdoe

This format, which focuses on one or more options at a time, is ideal for the rapid-fire mental retrieval required for the exam.

Conclusion

Mastering the vast set of commands for the RHCSA exam requires more than just practice; it requires a deliberate strategy for long-term memorization. The "forgetting curve" is the primary obstacle for every student, and traditional study methods are poorly equipped to overcome it. By combining two key methods, this challenge can be met. The first is consistent, hands-on lab practice in a real environment, which builds contextual understanding. The second is the use of Anki, which automates spaced repetition to ensure that information is reviewed at the precise moment it is about to be forgotten.

This process can be made even more effective by using generative AI to create the flashcard content. By providing a tool like Google's Gemini with specific prompts to be succinct, I was able to create a highly focused deck of flashcards that gets straight to the point. This combination of practical labs for muscle memory and AI-generated, efficient Anki cards for active recall provides a comprehensive and modern approach to passing the exam.

Links

Using Generative AI for RHCSA Anki Cards
+--Executive Summary
|  +--Strategy: Combine Labs (muscle memory) + Anki (spaced repetition)
|  +--Anki: Solves the "forgetting curve"
|  L--AI Role: Use Gemini for succinct flashcards (e.g., `useradd`)
+--Keywords
|  L--(List of terms: RHCSA, Anki, Generative AI...)
+--Glossary
|  +--Acronyms & Abbreviations
|  +--Commands (GNU/Linux)
|  +--Concepts (Learning)
|  L--Software & Technologies
+--Introduction
|  +--Goal: Memorize RHCSA commands via practiced repetition
|  +--Method: 1. Labs (practice) + 2. Flashcards (memorization)
|  +--Author's Setup: Linode lab + Anki (FOSS app)
|  L--AI Strategy: Tell Gemini to make answers "succinct and straight to the point"
+--The History of Anki
|  +--Creator: Damien Elmes (2006)
|  +--Origin: For learning Japanese (Name = "memorization")
|  L--Core: Spaced repetition (based on SuperMemo SM-2 algorithm)
+--The Problem That Anki Solves
|  +--Challenge: The "forgetting curve" (memory decay)
|  +--Problem: Traditional study (cramming) is inefficient
|  L--Solution: Anki automates spaced repetition (tracks easy/good/hard)
+--How to Use Anki Effectively
|  +--Key: Pair Anki with consistent lab practice
|  +--Workflow: 1. Do Lab (e.g., LVM) -> 2. Study Anki deck (e.g., `pvcreate`)
|  +--Benefit: Bridges understanding (lab) with memorization (Anki)
|  L--Alternative: Anki *before* lab as a warm-up
+--Examples of Flashcards
|  +--The AI Prompt: "Give me a much more efficient flashcard back."
|  +--Example 1: `useradd` (supplementary groups)
|  +--Example 2: `useradd` (default shell)
|  +--Example 3: `useradd` (specific UID)
|  +--Example 4: `useradd` (complex: primary/supplementary/shell)
|  L--Example 5: `useradd` (complex: comment/home/group)
+--Conclusion
|  +--Obstacle: "Forgetting curve"
|  +--Solution: 1. Labs (context) + 2. Anki (spaced repetition)
|  +--Amplifier: Generative AI (Gemini) for succinct cards
|  L--Final Strategy: Labs (muscle memory) + AI-Anki (active recall)
L--Links
   +--Anki Homepage
   L--Anki Flashcards page

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