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
Implement real-time communication between distributed AI agent instances using Redis Pub/Sub, covering channel design, message serialization, scaling patterns, and understanding delivery guarantees.
Learn how to minimize the delay between a user request and the first visible response from your AI agent by optimizing connections, DNS caching, request pipelining, and warm pool strategies.
Build an AI agent that detects code smells, suggests refactoring patterns, applies changes safely, and validates that behavior is preserved. A practical guide to automated technical debt reduction.
Learn how to build regression test suites for AI agent prompts, implement prompt versioning, generate diff reports, and integrate prompt testing into CI pipelines.
Navigate the evolving regulatory landscape for AI agents across the EU AI Act, US state laws, and emerging industry standards. Learn how agents are classified, what compliance obligations apply, and how to build regulation-ready agent systems.
RLHF is the training methodology that transforms raw language models into helpful, harmless assistants. Understand how it works, its variants like DPO and RLAIF, and the alignment challenges it addresses.
Build robust request validation for AI agent APIs using Pydantic v2 models, custom field validators, and discriminated unions. Learn how to handle nested agent configurations and return clear validation error responses.
Build intelligent caching layers for your AI agents using exact-match caches, semantic similarity caches, and time-based invalidation strategies to reduce costs and latency without serving stale responses.
Learn how to design retrieval-augmented prompts that dynamically inject relevant context, manage context windows efficiently, and produce grounded answers from external knowledge.
