Machine Learning & AI Topics
Production machine learning systems, model development, deployment, and operationalization. Covers ML architecture, model training and serving infrastructure, ML platform design, responsible AI practices, and integration of ML capabilities into products. Excludes research-focused ML innovations and academic contributions (see Research & Academic Leadership for publication and research contributions). Emphasizes applied ML engineering at scale and operational considerations for ML systems in production.
Generative AI and Large Language Models
The capabilities and behavior of modern generative and large language models. Covers how LLMs are pretrained, in-context learning and few-shot prompting, generative model families (autoregressive, diffusion), context windows, and tokenization and sampling. Emphasizes understanding what generative models can and cannot do and how they differ from discriminative ML.
LLM Evaluation and Observability
Measuring, monitoring and releasing generative and LLM-powered systems. Covers evaluation of open-ended outputs (human review, calibrated model-graded judges, reference-based and embedding scores, perplexity), hallucination, toxicity, privacy-leak and red-team checks, offline benchmarks versus online monitoring, and production observability (logs, traces, metrics, cost and token attribution, privacy-safe telemetry, drift and quality alerts). Also release decisions: A/B tests, canary rollouts, SLOs and go or no-go gates. Building the grounding architecture itself, calibration of predictive models and general software testing are covered elsewhere.
Prompt Engineering
Designing prompts and instructions that steer large language models toward reliable, controllable outputs without changing model weights. Covers zero- and few-shot prompting and example selection, chain-of-thought and step-by-step reasoning prompts, system, developer and role instructions and instruction hierarchy, output formatting and structured output (JSON schemas, templates, constraints), task decomposition and prompt chaining, managing context and token budgets inside a prompt, prompt injection as a design constraint, and iterating on, versioning and regression-testing prompts against a test set. Also covers prompt-level ways to reduce hallucination (grounding in supplied text, quoting support, allowing abstention) and unsafe, toxic or off-policy output (scoped roles, refusal templates), and prompting a model to generate constrained, validated output such as SQL. Boundary: excludes how models are pretrained and behave internally, sampling and decoding internals, fine-tuning, retrieval pipelines, and general LLM evaluation and monitoring.
Retrieval-Augmented Generation (RAG)
Grounding language-model outputs in external knowledge at inference time. Covers document ingestion, chunking and embedding, sparse, dense and hybrid retrieval (BM25, score fusion, contrastive training of retrievers), vector search and approximate-nearest-neighbour index engineering (HNSW, IVF, product quantization, filtering, updates, sharding), re-ranking with cross-encoders, and context assembly with prompt templates and citations. Also covers reducing hallucination through verification and abstention, evaluating retrieval-augmented systems, and production concerns such as freshness, access control, prompt injection through retrieved documents, deletion and privacy, caching, cost, and weighing retrieval against fine-tuning. Generic LLM evaluation and release gates, the craft of prompts beyond retrieved-context templates, attention internals, parameter-efficient fine-tuning, model serving and distributed inference engineering, database indexes, and whole-product ML system design are covered elsewhere.