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Use when auditing an LLM answer that carries source citations, to verify each claim is entailed by its cited sources and flag unsupported or misattributed ones.

Design conversational analytics interfaces: NL-to-SQL, query disambiguation, result narration, and data storytelling.

Use when a retrieval corpus has exact or near-duplicate docs/chunks inflating embedding cost or polluting top-k results, and you must detect and prune them.

Invoke when building or tuning a RAG/semantic-search pipeline's chunking: pick strategy, chunk size, and overlap validated against retrieval metrics.

Design ML experiment tracking: hyperparameter logging, model versioning, reproducibility, and experiment comparison workflows.

Design and implement ML feature stores: online/offline serving, feature freshness, training-serving skew prevention, and feature discovery.

Choose the right chart form for a dataset and question, then build an honest, labeled, accessible visualization; use whenever data needs plotting.

Plan ML model deployment: serving patterns, A/B testing, canary releases, monitoring, and rollback for production ML.

Build comprehensive model evaluation: benchmark selection, statistical significance, human evaluation protocols, and safety testing.

Design recommendation systems: collaborative filtering, content-based, hybrid approaches, cold start, and ranking.

Design data governance frameworks: data classification, lineage tracking, access policies, retention, and compliance mapping.

Curate and clean training datasets: deduplication, quality scoring, bias detection, and data augmentation strategies.

Systematically clean a messy dataset with profiling, ordered fixes, validation, and a provenance log; use before analyzing or merging untrusted data.

Design robust data pipelines: ingestion patterns, transformation stages, schema evolution, and data quality monitoring.

Design data quality monitoring: anomaly detection, schema validation, freshness checks, and data contracts between teams.

Design data warehouse schemas: star/snowflake schemas, slowly changing dimensions, materialized views, and query optimization.

Design document processing pipelines: OCR, layout analysis, information extraction, classification, and human review.

Choose embedding models and strategies: dimensionality, domain-specific models, multi-modal embeddings, and evaluation.

Design and scaffold idempotent, observable extract-transform-load pipelines with safe retries, backfills, and quality gates; use when building data pipelines.

Optimize model inference: quantization, distillation, batching, speculative decoding, and serving infrastructure tuning.

Plan and execute LLM fine-tuning: dataset preparation, method selection (LoRA, QLoRA, full), evaluation, and deployment.

Plan and execute safe database schema migrations: dependency ordering, rollback strategies, zero-downtime patterns, and data backfill procedures.

Diagnose slow SQL with execution-plan analysis and rewrite queries and indexes; use when a query is slow, times out, or burns database CPU.

Design real-time stream processing: windowing, watermarking, exactly-once semantics, and state management.