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Exam Buddy turns a student’s own notes into multiple-choice, written-answer, or mixed quizzes, then uses Gemma 3 4B through Ollama to generate and grade questions locally. Its design is a practical example of pairing a small local model with application-side validation and recovery logic—not a guarantee that generated questions or scores are always correct.
What Exam Buddy does
Kamal built Exam Buddy to help a friend prepare for an Operating Systems exam. Instead of drawing on generic OS trivia, it creates quizzes from the student’s notes, which may reflect course-specific emphasis, shorthand, or a professor’s comments.
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The project article describes three quiz formats: multiple choice, written answers graded by the model, and a combination of both. Learners can choose 5, 10, or 15 questions. Other features include themed explanations, follow-up questions, a retry mode for missed questions, score history, and a generated summary of what to review. Long notes are divided into parts so the tool can make separate quizzes from them.
How the local setup is put together
Kamal identifies Gemma 3 4B as the application’s only AI model and Ollama as its local inference route. The project began as a Python command-line script and later gained a browser interface. In the web version described in the post, a self-contained HTML file sends requests to Ollama’s local API using fetch.
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The author’s setup summary is to install Ollama, pull Gemma 3 4B, and serve the web interface with python -m http.server. The post describes no separate backend, framework, build step, API key, or remote AI API requirement. It points readers to the project README for detailed installation steps, so this summary is not a substitute for those instructions. Read Kamal’s project article on DEV Community.
What the implementation lessons reveal
The project’s early problems show why a local model alone is not a complete quiz application. The author describes adding input checks, output recovery, and application logic around model responses. These are observations from one project, not controlled tests of Gemma 3 4B across different computers or study materials.
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- Blank answers: In the command-line version, an empty response caused a crash when the program tried to parse it as an integer. A validation loop addressed this by accepting only numbered choices and asking again otherwise.
- Malformed JSON: Model responses sometimes included Markdown code fences or otherwise failed to match the expected structure. The author reports adding retries, cleanup, and object extraction to recover usable output.
- Long notes and question structure: A 16 KB note file proved difficult for the early version to turn reliably into structured questions, so the author split long notes into parts. The author also found that correct multiple-choice answers could appear repeatedly in the same position and shuffled choices after generation.
- Grading and request handling: The post describes fixing a streaming-request race and addressing a written-answer grading case in which “idk” received a score of about 75% despite feedback that contradicted that score. The figure is an anecdote about that implementation, not a general measure of grading accuracy.
- Validation and prompt injection: The author says validation was adjusted after it rejected usable question batches. The article also mentions an accidental prompt-injection test, but one anecdotal result cannot establish that the grader is resistant to prompt injection.
Together, these examples point to a useful distinction: the model proposes questions and grades responses, while the surrounding program must handle invalid input, inconsistent output, and interface behavior. The article does not establish a general accuracy rate for generated quizzes or written-answer scores.
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What “local” means for privacy
According to the author, inference runs locally through Ollama, so the AI layer does not require sending notes or answers to a remote AI API. Once Ollama and the model are installed, the tool can operate offline. The author also describes this approach as avoiding per-request AI API charges and API-key management for this personal project.
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That is a narrower claim than saying the app or computer is secure or private in every respect. The article does not report a security audit or establish how every part of a user’s machine or environment handles data. Local inference removes reliance on a remote AI API for the model request; it is not a blanket guarantee against data exposure.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Who this approach may suit
Exam Buddy’s design is most relevant to someone who wants practice grounded in their own course notes and is comfortable following the project’s README to set up Ollama and the web interface. Its modes cover recognition practice, free-response practice, or a mixture, while the review features help turn a quiz session into a list of topics to revisit.
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It is less appropriate to treat generated questions or model-assigned written scores as authoritative without checking them against the course material. The author’s reported fixes improve handling in this particular application, but they do not establish that every note file, answer, or local setup will behave reliably.
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