Practical notes on
AI & systems
Field notes from delivering AI systems integration, enterprise automation, RAG, cloud and IoT — not marketing fluff.
Managing and Versioning Prompts in Production
Prompts need the same operational discipline as code when AI features must survive model changes, audits, and real user traffic.
The Cost and Trade-Offs of Self-Hosting an LLM
Self-hosting an LLM is not just a GPU decision; it changes cost, operations, governance, and integration responsibilities.
Why Data Governance Is a Prerequisite for AI Adoption
Data governance is not paperwork; it is the engineering foundation that lets enterprise AI use data safely and reliably.
Connecting ERP Data Into an AI Assistant, Safely
A safe ERP-connected AI assistant needs clear data boundaries, enforced permissions, auditable access, and careful choices about retrieval.
Landing AI Agents in Enterprise Workflows, and Their Limits
AI agents can improve enterprise workflows when their scope, permissions, integrations, and review paths are engineered deliberately.
A Practical Architecture for AI Customer Service on LINE OA
A field-tested architecture view for connecting LINE OA to AI support, covering webhooks, RAG, integrations, handoff, and operations.
Choosing a Vector Database: pgvector, Qdrant or Others
A practical engineering guide to choosing between pgvector, Qdrant, search engines and managed vector databases for enterprise RAG systems.
Five common pitfalls when adopting RAG in the enterprise
Enterprise RAG succeeds when knowledge governance, retrieval design, permissions, evaluation, and operations are treated as core engineering work.
Three common reasons AI adoption fails, and how to avoid them
Most AI projects fail not because the technology is weak, but because the starting point is wrong. Three recurring causes we see across delivery projects, and how to avoid each.
RAG vs fine-tuning: which should companies choose?
Comparing RAG and fine-tuning across cost, maintenance, data security and update frequency — and the order most companies should choose in practice.
Three ways to move IoT data from edge to cloud
What are MQTT, Kafka and HTTP each good for? Choosing among three common patterns for getting IoT data to the cloud, by bandwidth, throughput, latency and integration effort.
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