Why RAG noise matters Retrieval errors and low‑quality matches inject irrelevant or misleading text into the LLM’s context; the model […]
Transformer Origins and Attention Basics Researchers shifted away from recurrent and strictly convolutional sequence models when they needed faster training
Why Optimize SQL? Poorly performing SQL directly harms applications: slower page loads, unhappy users, missed SLAs, higher infrastructure bills, and
Why normalization matters Normalization reduces redundancy and enforces consistent, unambiguous data models so updates, inserts, and deletes don’t create contradictions.
Measure Query Performance Accurate measurement starts with repeatable, objective metrics: wall‑clock (elapsed) time, CPU time, logical and physical reads, rows
Design goals and constraints Design decisions should prioritize correctness and predictable performance. Enforce data integrity with appropriate types and database
Embedding fundamentals and intuition Embeddings turn text into numeric vectors so machines can reason about meaning: similar phrases map to
Define goals and success metrics Start by tying analytics objectives to concrete business outcomes: what decision will change, who will
Define quality metrics and thresholds Start by selecting a small set of measurable quality dimensions tied to business impact: accuracy