Why AI-native PLM is emerging in 2026: LLM copilots for BOM cleansing, requirements, and engineering search - and the data architecture that makes it work.
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.
An applied defense-in-depth pattern for agentic AI security: the indirect prompt injection kill-chain, OWASP LLM/Agentic Top 10, and layered mitigations.
In two weeks of June 2026, ~12 frontier open-weight models shipped — GLM-5.2, MiniMax M3, DeepSeek V4.1, Qwen 3.7. What it means for cost, moats, and strategy.
GLM-5.2 benchmark analysis: Z.ai's 753B MoE under MIT license, coding and agentic results vs GPT-5.5 and MiniMax M3, cost-per-token, and where it fits.
Corrective RAG (CRAG) and Self-RAG explained for 2026: retrieval grading, query rewriting, self-reflection loops, a reference design, and when each pays off.
The LLM semantic router pattern in 2026: route requests by intent and cost to the right model, with vLLM Semantic Router, embeddings, and a reference design.
A 2026 benchmark of LLM JSON mode and constrained decoding: throughput, latency, and accuracy across grammar-based methods, with reproducible methodology.
A 2026 text-to-SQL benchmark methodology: execution accuracy, schema linking, latency, and cost across model tiers - plus where generated SQL goes wrong.
How LLM prompt caching works in 2026: provider-side vs self-hosted KV reuse, cache-aware prompt design, hit-rate economics, and where it quietly breaks.