Semantic query engines treat foundation-model filter, join, classify, map, and rank as first-class operators—logical and physical plans, cost–quality tradeoffs, SemBench workloads, and how they differ from script-style RAG and vector search alone.
Scaling DataFrames: when notebook habits meet distributed execution—pandas semantics, Modin’s compiler stack, Snowflake ordering, Parquet pushdown, quote-aware CSV, Ray data movement, and what is verified vs. speaker opinion.
Retrieval list diversification: geometric post-processing, evaluation gaps, and RAG context budgets—MMR, MSD, DPP, Cover, and SSD as NumPy reranking after any Python retrieval stack.
REFRAG compresses retrieved passages into chunk-level decoder positions, then uses RL to selectively expand high-entropy spans—mechanisms, training pipeline, and how to read TTFT and RAG benchmarks without over-generalizing paper numbers.
Query Agent on a vector database: auditable retrieval, Ask vs Search modes, schema introspection, multi-collection routing, and what is verified in docs versus speaker claims.
Multi-vector search: how to choose among single-vector bi-encoders, late interaction (ColBERT-family), and cascaded reranking—grounded in the Weaviate podcast with LightOn’s Amélie Chatelain and Antoine Chaffin.
Multi-stage language programs and automatic prompt optimization: from DSPy to MIPRO—proposal, bootstrapping, and combinatorial search; credit assignment; meta-proposers; and how they relate to RAG, agents, and fine-tuning.
Judge-time compute: stacking structured, composable weak-model calls at evaluation time instead of assuming one expensive judge pass is enough—Verdict, agreement metrics, and production guardrails, with evidence boundaries called out.