Insights on AI development, LLM systems, offshore engineering, and automation for companies in the US, Canada, Australia, Singapore, and Japan.
Showing 37–48 of 117 posts
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, deployment, cost optimization. From 30+ NKKTech deployments.
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 — based on 20+ production RAG deployments by NKKTech.
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 50+ production deployments at NKKTech.
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.
Practical, code-level guide to building an eval framework for AI agents that you'll actually maintain. Frozen eval sets, scoring functions, component-level evals, and regression tracking — the same approach NKKTech ships with every production agent.
Honest production comparison of the four vector databases that matter in 2026: Pinecone, Weaviate, pgvector, Qdrant. Latency, cost, operational complexity, scaling characteristics, and which to pick for which workload — drawn from NKKTech RAG deployments.
Practical guide to the three metrics that actually matter for evaluating production RAG systems: retrieval precision, faithfulness (no hallucination), and answer relevance. How to measure them, what targets to aim for, and how to debug regressions.
Concrete HIPAA compliance checklist for AI systems that process PHI: BAA requirements, data flow architecture, audit logging, access controls, and the gotchas with US-based LLM providers. Practical, not legalese.
Practical GDPR compliance guide for AI systems serving EU users: lawful basis, DPIA requirements, data minimization, the right to erasure for training data, and how the EU AI Act overlay changes the calculus in 2026.
Ten battle-tested prompt engineering techniques used in production AI systems in 2026: chain-of-thought, few-shot, structured output, constitutional AI, prompt caching, and more. Concrete examples and when to use each.