Synthetic Data for LLM Fine-Tuning: Generation, Quality Filtering and Avoiding Model Collapse
Synthetic data for LLM fine-tuning explained: self-instruct, distillation, rejection sampling, quality filters, decontamination and how to avoid model.
PII Detection and Redaction for LLM Applications: Presidio, NER and Reversible Tokenization
PII redaction for LLM applications explained: detection with regex, NER and Presidio, reversible tokenization, latency budgets, recall trade-offs and a.
Ling 3.1 Flash Explained: Architecture, Pricing and Benchmarks
Ling 3.1 Flash explained: what is documented on context, access and pricing, what is not disclosed, and how to evaluate it against peer models.
MiniMax M3.1 Flash Preview Explained: Architecture, Pricing and Benchmarks
MiniMax M3.1 Flash Preview explained: documented context, effort levels, access and pricing, what is still unpublished, and how to evaluate it.
LLM-Generated PLC Code: Safety, Verification and Guardrails
Using LLMs to generate IEC 61131-3 Structured Text for PLCs: verification gates, simulation, IEC 61508 constraints, and where it is unsafe. Reference pipeline.
Agentic RAG Architecture Patterns: When Plain RAG Is Not Enough
Agentic RAG architecture patterns for 2026: planner-executor retrieval loops, tool-augmented search, and when plain RAG breaks down.
Google Gemini 3.5 Flash Explained: Architecture, Benchmarks, and Deployment (2026)
Google Gemini 3.5 Flash explained: the MoE multimodal architecture, context window, real 2026 benchmarks, pricing, latency, and how it compares to GPT and Claude.
Agent Benchmarks in 2026: SWE-bench Verified, GAIA, and tau-bench
A deep dive into 2026 AI agent benchmarks: SWE-bench Verified, GAIA, and tau-bench — what they measure, how they leak, and how to read agent leaderboards honestly.
AI-Native PLM: How LLMs Are Reshaping Engineering Data
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.
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.