Why RAG noise matters Retrieval errors and low‑quality matches inject irrelevant or misleading text into the LLM’s context; the model then treats that material as authoritative. That single failure mode cascades: factual contradictions and hallucinations become more frequent, answers drift off-topic, and confidence scores no longer correlate with truth. Practically,

Transformer Origins and Attention Basics Researchers shifted away from recurrent and strictly convolutional sequence models when they needed faster training and better handling of long-range dependencies. The breakthrough replaced recurrence with a mechanism that lets every token directly attend to every other token in a sequence, enabling full pairwise interaction

Why Optimize SQL? Poorly performing SQL directly harms applications: slower page loads, unhappy users, missed SLAs, higher infrastructure bills, and database contention under load. Optimizing queries reduces latency, cuts I/O and CPU usage, improves concurrency, and lets the same hardware support more traffic and analytical workload with fewer trade-offs. Small

Why normalization matters Normalization reduces redundancy and enforces consistent, unambiguous data models so updates, inserts, and deletes don’t create contradictions. By separating entities (for example, keeping customers and orders in distinct tables rather than copying address data into every order row) you avoid update anomalies, shrink storage for repeated values,

Measure Query Performance Accurate measurement starts with repeatable, objective metrics: wall‑clock (elapsed) time, CPU time, logical and physical reads, rows processed, execution count, and wait/lock events. Capture both the execution plan and runtime statistics so you can compare estimated vs actual costs and spot plan changes. Run queries multiple times

Design goals and constraints Design decisions should prioritize correctness and predictable performance. Enforce data integrity with appropriate types and database constraints (primary keys, foreign keys, unique and check constraints) so the schema itself prevents invalid states. Define clear access patterns and SLOs early: optimize for the most frequent queries rather

Embedding fundamentals and intuition Embeddings turn text into numeric vectors so machines can reason about meaning: similar phrases map to nearby points in high‑dimensional space. Distance (commonly cosine similarity) reflects semantic relatedness rather than literal token overlap, which lets you match paraphrases, cluster topics, and rank search results. Think of

Define goals and success metrics Start by tying analytics objectives to concrete business outcomes: what decision will change, who will act on it, and how much value that change creates. Convert those outcomes into 3–5 measurable success metrics that mix business impact (conversion lift, retention delta, revenue per user) with

Define quality metrics and thresholds Start by selecting a small set of measurable quality dimensions tied to business impact: accuracy (correct values), completeness (missingness), consistency (cross-field and cross-source agreement), uniqueness (duplicates), validity (schema/type conformance) and timeliness (freshness). For each dimension define a numeric metric (e.g., percent nulls, duplicate rate, schema-mismatch