Prompt engineering, RAG, and fine-tuning are not competing product categories. They are different control surfaces: prompts shape instructions, RAG supplies governed knowledge at inference time, and fine-tuning changes repeated model behavior. The right enterprise AI architecture starts with the failure mode, then adds retrieval, training, evaluation, access control, observability, auditability, and lifecycle ownership only where risk and requirements justify them.
AI-assisted software delivery is not just a coding-assistant rollout. When generation gets faster, the constraint often moves to context, tests, review, ownership, governance, and maintenance. This post lays out an AI-aware SDLC operating model.
AI made code cheap, but judgment across DevOps, data, infra, and services is still scarce. Why full-stack architects will define the next decade of software.
Sending prompts to hosted AI APIs means your source code, customer data, and internal documents leave your perimeter, protected only by a vendor’s promise. This post makes the blunt case for running open-weight models in your own cloud account, using Kimchi for the model layer and spend controls and Cast AI to autoscale GPUs on demand. It covers the real costs and objections, a way to check the economics on your own workload, and a six-step rollout starting with your most sensitive data.
A practical enterprise decision guide for choosing among rules, BI, predictive analytics, traditional ML, deep learning, generative AI, RAG, and controlled AI agents based on workflow fit, data readiness, cost, risk, and governance.
Cloud-native application development is not just running software in the cloud. It is an operating and design discipline for systems that must change while they run: consistent packaging, automated delivery, dynamic infrastructure, telemetry, resilience, and ownership.
OpenTelemetry is best understood as a standard telemetry pipeline: APIs and SDKs create signals, context propagation links work across services, semantic conventions make data consistent, OTLP transports it, Collectors process it, and exporters deliver it to observability backends.
Self-hosting open source LLMs does not remove governance work. It moves ownership to the control planes around the model: identity, routing, retrieval, Kubernetes runtime policy, observability, audit, and cost.
Coding-agent cost is not mainly the price of one clever prompt. It is the recurring cost of moving repository state, tool output, and loop history through paid models until useful work is accepted. Gateway observability makes that spend attributable and governable, while agent-loop discipline determines how much context gets sent.
Tool-using AI agents need more than prompt guidance. If an action can create a real side effect, enforcement should live in executable policy that can allow, deny, stop, or escalate before the tool call happens.