LLM Observability and LLMOps: Tracing, Evals, Drift
An LLM observability and LLMOps architecture: OpenTelemetry GenAI traces, spans, online evals, and drift detection for production LLM and agent systems.
GPT-5.6 Explained: OpenAI’s Sol, Terra, and Luna
GPT-5.6 explained: OpenAI's Sol, Terra, and Luna tiered family - architecture signals, reasoning modes, benchmarks, pricing, access, and how it compares in 2026.
NVIDIA GB300 NVL72: Blackwell Ultra Architecture (2026)
NVIDIA GB300 NVL72 explained: Blackwell Ultra GPUs, the 72-GPU NVLink rack, memory and power, and how it scales AI training and inference at rack level in 2026.
Embedding Models Benchmark: OpenAI, Cohere, Voyage, BGE
A 2026 embedding models benchmark: OpenAI, Cohere, Voyage, and BGE on retrieval quality, dimensions, cost, and MTEB - with what changed for 2026.
AI Inference Cost Optimization: GPU FinOps in 2026
An AI inference cost optimization decision record: continuous batching, KV-cache, quantization, speculative decoding, spot GPUs, and autoscaling the inference path.
LLM Gateway Architecture: The Control Plane for AI Apps
An LLM gateway architecture for production AI: routing, semantic caching, rate limits, budgets, fallbacks, and observability across multiple model providers.
Fine-Tuning vs RAG vs Long-Context: A 2026 Cost/Quality Decision
A 2026 cost and quality decision record for fine-tuning vs RAG vs long-context LLMs: token economics, latency, accuracy trade-offs, and a decision matrix.
Image Segmentation Models: Types, Comparison, and Uses (2026)
A 2026 technical overview of image segmentation models: semantic, instance, and panoptic segmentation, U-Net to SAM 2, with a comparison and applications.
Agentic AI Security: Defeating Prompt Injection in 2026
An applied defense-in-depth pattern for agentic AI security: the indirect prompt injection kill-chain, OWASP LLM/Agentic Top 10, and layered mitigations.
Corrective RAG and Self-RAG: Architecture Patterns (2026)
Corrective RAG (CRAG) and Self-RAG explained for 2026: retrieval grading, query rewriting, self-reflection loops, a reference design, and when each pays off.