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
Learn how to decompose complex AI tasks into sequential prompt chains — passing intermediate results between LLM calls, handling errors in pipelines, and building reliable multi-step workflows.
Master the art of composing multi-layer prompts dynamically. Learn composition patterns, priority ordering strategies, token budget allocation, and techniques for building modular prompt pipelines.
Learn practical prompt compression techniques including LLMLingua, selective context pruning, and abstractive compression to cut token costs while preserving output quality.
Master prompt compression methods including LLMLingua, selective context pruning, and abstractive compression to halve your token costs while maintaining output quality. Practical Python implementations included.
Master the fundamentals of prompt engineering — learn to write clear system and user messages, format instructions for consistency, and avoid common pitfalls that cause unreliable LLM outputs.
Learn to build robust prompt guardrails that enforce safety policies, prevent instruction override attacks, and maintain consistent agent behavior. Covers layered safety architecture and testing strategies.
Understand the different types of prompt injection attacks targeting AI agents, see real-world examples of direct and indirect injection, and learn why agent security must be a first-class engineering concern.
A practical guide to migrating prompts across LLM providers. Covers provider-specific differences, migration checklists, abstraction layers, and testing strategies to ensure consistent behavior after switching.
Build comprehensive observability for your AI prompts. Learn structured prompt logging, performance tracking dashboards, failure analysis workflows, and data-driven optimization techniques.
