I Built Student Memory Into Groq Prompts Via Hindsight

I Built Student Memory Into Groq Prompts Via Hindsight I added twelve lines of Hindsight retrieval to the Groq prompt pipeline and the AI's study plan recommendations went from generic topic lists to specific, session-aware guidance. The model didn't change. The context did. The setup StudyMind is a study tool built for college students. Upload a PDF or TXT file and the app generates a 5-question multiple-choice quiz, 10 spaced-repetition flashcards, and a personalized study plan — all via Groq's Llama 3.3 70B. Answers are stored in Supabase. Hindsight sits between the database and the prompt layer, turning raw event logs into context the model can reason about. The problem I kept running into wasn't inference quality. Groq generates excellent questions. The problem was that every prompt was a clean slate. A student could fail the same concept three sessions in a row and the model would have no idea — because nobody told it. This article is about how I fixed that, what the prompt layer