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
Architect a single chat agent that operates across web widgets, WhatsApp, Slack, and SMS by building a channel abstraction layer, message normalization pipeline, and channel-specific rendering adapters.
Manage AI agent configurations across multiple Kubernetes clusters using GitOps workflows, config synchronization, drift detection, and environment promotion pipelines.
Learn how to build a multi-index RAG system that routes queries to appropriate collections, merges results, and normalizes relevance scores across heterogeneous document stores.
Learn how to detect and handle multiple intents in a single user message, including intent splitting, parallel processing, and delivering coherent ordered responses.
Build a multi-language AI support agent with automatic language detection, real-time translation, culturally adapted responses, and quality assurance pipelines that maintain accuracy across all supported languages.
Implement cross-lingual semantic search that lets users query in one language and retrieve results in any language, using multilingual embedding models that map all languages into a shared vector space.
Explore how AI agents are evolving beyond text-only interfaces to incorporate voice, vision, and physical interaction. Learn about modality fusion, embodied agents, spatial computing integration, and the design principles for multi-modal agent systems.
Master multi-modal prompting techniques that combine text, images, and code inputs in a single prompt to unlock more capable and context-rich LLM interactions.
Deploy AI agent systems across multiple geographic regions with data replication, intelligent DNS routing, automated failover, and region-aware architecture that delivers sub-200ms response times to users worldwide.
