This article explains why hallucinations happen, the types, and practical ways to reduce them using RAG, low temperature, guardrails, and validation layers.
CI/CD pipelines are essential, but they carry risks if not designed correctly. This post discusses common security mistakes and shares practices to avoid them.
Stop treating AI agents like prompts — treat them like software. To ship in 2026: validated tool contracts, tiered memory, RAG grounding, and deep observability.
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.