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Field notes on AI strategy, agents, custom models, and the infrastructure that keeps them running. No hype.
Hallucination rates still run from 15 to 52 percent across models in 2026. Here is a practical, layered approach to reduce LLM hallucinations in enterprise systems that ship to real users.
Your RAG chunking strategy affects accuracy more than your vector database does. Here is what the 2026 benchmarks say about chunk size, overlap, and the splitter to start with.
Multi-agent orchestration is the dominant architecture story of 2026, but more agents is not always better. Here is when a multi-agent system pays off and how to govern one in production.
Model Context Protocol has moved from experiment to production standard in 2026. Here is what MCP is, why enterprises are adopting it, and how to use it without creating new risk.
LLMOps is now a multi-billion dollar category for a reason. Here is what LLMOps services actually cover, how they differ from classic MLOps, and when bringing in help pays off.
LLM evaluation has become a production gate, not a research checkbox. Here is how to build evals that catch regressions before users do, including where LLM-as-a-judge fits.
The EU AI Act's biggest deadline lands on 2 August 2026. Here is what changes for transparency and general-purpose AI, and how to build AI agents that stay compliant.
A clear breakdown of AI agent development cost in 2026, from simple assistants to enterprise multi-agent systems, plus the three-year total that most quotes leave out.
The prototype is the easy part. Reliability, cost, and security are where AI projects quietly die, and how to get past it.
Self-hosting open models looks cheaper until you add up GPUs, idle time, and engineering. Here is the honest breakeven math and when running your own models actually pays off.
A practical guide to LLM observability and production monitoring, covering tracing, evals, and drift detection so your AI system fails loudly instead of silently.
Token prices have fallen fast, but wasted tokens still cost real money. A practical guide to LLM inference cost optimization, from caching to model routing, for teams running AI in production.
Most enterprise AI agents stall before they ever run for real users. Here is the engineering work that gets an agent from pilot to production, and why so many teams skip it.
A practical breakdown of when to hire an AI agency and when to build in-house, with the real costs, timelines, and trade-offs for technical founders and engineering leads in 2026.
Fine-tuning vs RAG is the wrong fight. Here is how to decide when to fine-tune an LLM, when retrieval is enough, and why most production systems in 2026 use both.
Enterprise AI agents have crossed into mainstream production with strong ROI. A grounded look at the returns, the payback timelines, and which agent to build first.
RAG fetches relevant chunks. Production needs information that is relevant, trustworthy, and auditable. Here is why context engineering, not RAG by itself, is what makes grounded AI reliable.
How to choose AI consulting services that actually ship, with the questions to ask, the red flags to avoid, and what senior, no-lock-in delivery should look like.
AI agent adoption has outpaced security. A practical guide to AI agent security and governance in 2026, covering identity, guardrails, and the controls risk teams now require.
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