Agentic AI, LLM engineering, and the models behind modern automation — multi-agent systems, LLM evaluation and comparisons, RAG, fine-tuning, AI infrastructure, security, and production AI engineering.
From the blog
Build a production-grade pipeline for embedding and ingesting millions of documents into a vector database, covering chunking strategies, parallel processing, rate limiting, and progress tracking.
Learn when and how to use batch processing to cut AI agent costs by up to 50%. Covers batch API usage, queue-based architectures, priority tiers, and SLA tradeoffs for non-time-critical agent workloads.
Academic benchmarks do not predict production performance. Learn which evaluation metrics actually matter for deploying LLMs, how to build task-specific evaluation suites, and why human evaluation remains essential.
Establish a rigorous benchmarking and profiling practice for your AI agents using structured test suites, profiling tools, baseline metrics, and regression tracking to maintain and improve performance over time.
Learn how to rigorously benchmark vector databases with proper methodology — measuring latency, throughput, and recall under realistic conditions to make informed infrastructure decisions.
Learn how to detect, measure, and mitigate bias in AI agent systems using statistical testing frameworks, counterfactual analysis, and continuous monitoring pipelines.
Biomolecular AI foundation models predict protein structures, decode genomic sequences, and accelerate drug discovery. Learn how biological language models are transforming life sciences research.
Learn the blackboard architectural pattern for multi-agent AI coordination. Build a shared knowledge space where specialized agents contribute partial solutions that converge into complete answers.
Implement blue-green deployment strategies for AI agent services to achieve zero-downtime updates, safe model swaps, traffic splitting, and instant rollback for prompt and model changes.
