@bashifuirkashi: 15 RAG concepts every AI engineer should know 👇 1. Chunking (how you cut up your docs) 2. Chunk overlap (so you don't split a sentence in half) 3. Embeddings (turning text into numbers) 4. Vector databases (where those numbers live) 5. Similarity search (finding the closest match) 6. Top k (how many chunks you pull per question) 7. Hybrid search (keywords plus meaning) 8. Metadata filtering (search only what matters) 9. Query rewriting (fixing a vague question first) 10. HyDE (guess the answer, then go find it) 11. Reranking (put the best chunk on top) 12. Small to big (find the line, send the section) 13. Contextual retrieval (give each chunk its backstory) 14. Grounding and citations (show the receipts) 15. Retrieval eval (proving it actually works) If you don't understand at least seven of these, you gotta study up. Go to the link in my bio on TikTok for the resources of the full breakdown
