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 real-time AI agent interfaces using WebSocket connections in FastAPI with connection lifecycle management, heartbeat mechanisms, and structured message protocols.
AI guardrails enforce safety boundaries, filter harmful content, and prevent unauthorized actions. Discover the frameworks enterprises use to deploy AI responsibly in 2026.
Learn what defines an AI agent, how it differs from a chatbot, and explore the three core components — perception, reasoning, and action — that make autonomous AI systems work.
Understand what large language models are, how they evolved from simple neural networks to GPT-scale transformers, and why they can generate human-quality text.
Physical AI combines embodied intelligence with world models so robots can perceive, reason, and act in unstructured environments. Learn how it works and why it matters.
Understand what Retrieval-Augmented Generation is, why it exists, how the core architecture works, and when to choose RAG over fine-tuning for grounding LLM responses in your own data.
Learn how to build observability into AI agent orchestration systems. Covers dashboard design, metric collection, alert rules, trace correlation, and debugging strategies for agent workflows.
Learn how to version and migrate AI agent workflows safely. Covers versioning strategies, backward compatibility patterns, migration techniques, and rollback procedures for zero-downtime updates.
Understand the key differences between zero-shot, one-shot, and few-shot prompting. Learn when each technique works best and how to select high-quality examples for reliable LLM outputs.
