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Feature Requests

Recall Review/Quiz 2.0
Quiz Revamp: More Question Types, Better Creation Flow, Custom Settings Our current quiz feature has significant room for improvement. We're proposing a comprehensive overhaul that would transform it into a more flexible and personalized learning tool. Expanded Question Types Right now we're limited to basic multiple-choice. We could expand to include: Open-ended questions for deeper comprehension testing Flashcards for rapid review Matching exercises to reinforce concept connections Other formats suited to different learning styles and content types Improved Question Creation Experience The current quiz creation flow could be much more efficient: Bulk question generation by tag – create comprehensive quizzes around specific topics instantly Tag-based question review – organize and manage questions by subject for more focused practice sessions Customizable Review Settings Give users more control over their review experience: Set question count – choose session length based on available time Shuffle questions – randomize order to test true retention Additional customization options for flow and format Gamification & Streaks Layer in motivational elements to encourage consistent practice: Quiz streaks tracking Other engagement features that make daily reviews more rewarding This would elevate Recall's quiz from a basic feature into a genuinely personalized learning system that adapts to individual needs and preferences.
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Recall Review / Quiz
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Processing of larger PDF documents
During testing, large PDF documents (NIST IR 8428 or other research articles for example) exceed the token output window of the LLM that's in use. I suggest one of two recursive solutions: separate large documents into smaller parts and create individual cards for each part. a. Bad: may create section headers that don't make sense when attempting to determine the original document those section headers were created from. Goes against what appears to be the general theory behind Recall. b. Good: Summaries themselves maintain the maximum output token window for each section. Page and summary separation and ultimate compression to a maximum token output window. a. Bad: Maximum token output requires total compression of all previous summaries into one condensed summary. Substantial context may be lost due to the lack of reference back to the original source material when compressing summaries. b. Document is kept in one card. An example pseudo-function for option 2 is as follows: FUNCTION summarizeLargePDF(pdfFilePath, maxInputTokens, maxOutputTokens, llm): Initialization currentPage = 1 totalTokens = 0 currentSummary = "" allSummaries = [] # Array to hold section summaries sectionStartPage = 1 Recursive Page Processing Function (Nested) FUNCTION processPage(pageNumber): pageText = extractTextFromPage(pdfFilePath, pageNumber) pageTokens = countTokens(pageText) Check if adding this page exceeds input token limit IF (totalTokens + pageTokens) > maxInputTokens: Summarize the section sectionEndPage = pageNumber - 1 sectionText = extractTextFromPages(pdfFilePath, sectionStartPage, sectionEndPage) sectionSummary = llm.summarize(sectionText, maxOutputTokens) Add summary to the list and reset section allSummaries.append(sectionSummary) sectionStartPage = pageNumber totalTokens = 0 totalTokens += pageTokens currentPage += 1 Recursive call for the next page processPage(currentPage) Main Function Logic processPage(currentPage) # Start recursive processing Final Summarization of all Section Summaries WHILE allSummaries.length > 1: Combine and re-summarize in pairs newSummaries = [] FOR i = 0 TO allSummaries.length - 1 STEP 2: combinedText = allSummaries[i] + allSummaries[i+1] newSummary = llm.summarize(combinedText, maxOutputTokens) newSummaries.append(newSummary) allSummaries = newSummaries The single remaining summary is the final result finalSummary = allSummaries[0] RETURN finalSummary
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