Insights on AI development, LLM systems, offshore engineering, and automation for companies in the US, Canada, Australia, Singapore, and Japan.
Showing 49–60 of 113 posts
Four chunking strategies for production RAG systems: fixed-size, semantic, recursive-character, and hybrid. When each works, when each breaks, and how chunk size affects retrieval precision + cost. With benchmark numbers from NKKTech deployments.
Practical APPI (Japan's data protection law) compliance guide for AI systems processing personal data of Japanese users. PPC notifications, opt-in consent requirements, cross-border transfer rules, and the 2024 AI Act overlay.
Context engineering — the practice of curating what goes into an LLM's context window — has surpassed prompt engineering as the highest-leverage skill for AI engineers in 2026. Patterns for retrieval, memory, summarization, and dynamic context assembly.
Three production patterns for orchestrating AI agents: hub-and-spoke, swarm, hierarchical. When each works + breaks + failure-mode diagnostics from real deployments.
Pure semantic search beats keyword search on ~70% of queries — and gets crushed on the other 30%. Production hybrid retrieval combines BM25 + dense vectors + reranking. The decision framework, with real eval numbers from NKKTech RAG deployments.
Cross-border AI data transfers under GDPR, APPI, PIPEDA, PDPA: the contractual mechanisms (SCCs, BCRs), the adequacy decisions, and the practical impact on AI vendor selection. Real templates and gotchas from NKKTech client deployments.
Production decision framework for LoRA, QLoRA, and full fine-tuning. The eval numbers that matter, the compute cost tradeoffs, and when each method actually wins on real client workloads. From NKKTech fine-tuning deployments.
Production-ready AI agents 2026: memory, tool calling, multi-agent orchestration, eval frameworks, human-in-the-loop, deployment, cost optimization.
Production RAG isn't a notebook with LangChain and Pinecone. Deep technical playbook covering chunking, embeddings, vector database choice, hybrid retrieval, generation layer, evaluation, operations, and cost — from NKKTech's production RAG work.
Practical implementation guide for building AI systems compliant with HIPAA (US), GDPR (EU), PIPEDA (Canada), PDPA (Singapore), and APPI (Japan). Technical patterns, audit log requirements, right-to-explanation, deletion, cross-border data transfer — from a Vietnam-headquartered engineering group with ISO 9001 and 22301 certifications.
When do you fine-tune an LLM, build a RAG system, or stay with prompt engineering? Practical decision framework with cost, latency, and quality tradeoffs from NKKTech's production deployments.
Honest production comparison of the three dominant multi-agent frameworks in 2026: LangGraph, CrewAI, and Microsoft AutoGen. Performance, debuggability, persistence, cost, and which to choose for B2B AI workloads — drawn from NKKTech deployments.