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 complete voice-powered appointment booking system using Twilio, speech-to-text, text-to-speech, calendar integration, and intelligent booking logic with a FastAPI backend.
Set up CDN and edge caching for your AI agent's static assets, API responses, and pre-computed results to reduce global latency with proper cache headers, edge functions, and geographic optimization.
Implement the Chain of Responsibility pattern for AI agents with fallback chains, capability matching, and cost-optimized ordering to handle requests efficiently.
Learn how chain-of-thought (CoT) prompting dramatically improves LLM accuracy on reasoning tasks. Covers manual CoT, auto-CoT, tree-of-thought, and when step-by-step reasoning helps most.
Discover how to apply chaos engineering to AI agent systems by designing controlled failure experiments, measuring blast radius, defining steady state, and building confidence in agent resilience under real-world conditions.
Implement secure authentication for chat agents with JWT tokens, anonymous-to-authenticated session linking, user identification strategies, and session security best practices for production deployments.
Master the techniques for managing conversation context in chat agents, including context window optimization, message pruning strategies, summarization, and topic tracking for coherent multi-turn interactions.
Master the design patterns for chat agent conversation flows including state machines for guided flows, free-form handling for open-ended conversations, and hybrid approaches that combine structure with flexibility.
Build robust fallback systems for chat agents that detect out-of-scope questions, provide helpful redirects, escalate to humans intelligently, and learn from failures to continuously improve coverage.
