Using RAG to Study Interview Notes
Ideal for coding and system design interview prep.
Retrieval-Augmented Generation (RAG) is an AI technique that combines retrieval of relevant data (like your interview notes) with generation of precise, grounded answers.
When to Use
Use RAG when you have large collections of interview notes or Q&A logs and need personalized, fact-based answers. It’s perfect for revising past mock interviews, coding feedback, or system design insights.
Example
If you store all your mock interview notes, a RAG-powered assistant can answer, “What are my weak areas in dynamic programming?” by referencing your actual notes instead of guessing.
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Why Is It Important
RAG ensures your learning is grounded in your real experiences. It reduces AI hallucinations and gives targeted, personalized revision—especially before FAANG-level interviews.
Interview Tips
Explain that RAG merges retrieval and generation to improve accuracy. Highlight how it enhances interview preparation efficiency and knowledge retention.
Trade-offs
RAG boosts relevance and accuracy but can be slower or require more setup than standard AI chat models.
Pitfalls
Feeding irrelevant or unstructured notes can confuse retrieval. Always curate clean, labeled content to get meaningful insights from RAG.
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