RAG failures stem from retrieval, not models. Replace one-size-fits-all vector search with a decision framework, hybrid flow, and guardrails for reliable systems.
Decouple heavy processing with Spring Boot, Kafka, and WebSockets: AI consumers analyze events asynchronously, while WebSockets deliver real-time insights to users.
Classify requests (dashboards vs exploration/jobs), cap and prioritize concurrency, and fall back to cache/rollups so critical dashboards stay responsive during spikes.
Learn how to implement your first AI agents with the help of RubyLLM. Define a chat interface with access to a set of SERP tools that LLM models can use in their work.
Distributed AI systems fail faster than humans can respond, making traditional response insufficient. Self-healing systems use telemetry and automation to recover early.
AI-driven development expands attack surfaces; this article shows how continuous security, zero trust, and runtime enforcement scale DevSecOps in AI pipelines.
Responsible AI is an engineering problem and not a policy document. It must be mandatory to ensure AI systems are designed, built, and used responsibly.
Agentic AI transforms DevOps from reacting to incidents to systems that understand, decide, and act on their own, reducing toil and enabling autonomous infrastructure.