Intent-based chaos engineering tests AI systems with calculated stress, using topology, sensitivity, and SLA insights to ensure predictable resilience.
RAG alone doesn’t stop hallucinations. I use five guardrails: relevance scoring, forced citations, NLI checks, staleness detection, and confidence scoring.
A secure MCP server can still break production. Twenty heuristic rules score readiness by catching missing timeouts, unsafe retries, and absent error schemas.
At GTC 2026, Jensen Huang, Aravind Srinivas, Harrison Chase, Mira Murati, and Michael Truell made a compelling case that the future of AI belongs to open agent systems, not just open models.
Learn about agentic AI, its autonomous capabilities, and emerging security threats, including memory poisoning, API misuse, and multi-agent vulnerabilities.
Create a zero-cost AI application quickly using Ollama and Java with Spring AI — with no extra costs and full compatibility with other LLMs like OpenAI.
Build a 3-agent research analysis swarm where you can swap models, tweak prompts, and compare orchestrator performance without duplicating configuration.
Conversational AI memory fails at scale because it’s state, not a model feature. Treat it as a governed, layered, distributed infrastructure, not prompts.
AI systems can be fully “up” yet behave unpredictably, expensively, or incorrectly. Observability must track job state, retries, token usage, and cost.
Vector RAG is good for semantically similar retrieval, but GraphRAG adds missing entity relationships and reasoning required for deterministic entity grounding.
Build a pipeline that extracts structured fields from raw transcripts (sentiment scores, urgency signals, buying intent) and feeds them straight into your ML models.
CORE
