applied AI systems and technical analysis
IT related articles from Kyle Beyke
This lesson explains why production prompting is different from consumer chat prompting. It shows how to design prompts as operational specifications with explicit tasks, grounded context, structured outputs, guardrails, examples, and version control so business AI systems behave more reliably in production.
Large language models may be extremely useful without being a clear path to artificial general intelligence. This article explains what today’s systems do well, where they still break down, why benchmark gains can mislead, and how to think about AI strategy without buying into AGI hype.
LLM integration is one of the most important fundamentals in modern AI development. Before you build retrieval, agents, workflows, or polished product features, you have to solve a…
AI in retail is no longer confined to forecasting engines or back-office automation. It now sits much closer to the customer and the store, helping retailers connect physical locations, ecommerce, service, merchandising, and fulfillment into a more seamless phygital experience. This guide explains where AI is creating practical value in modern retail, where it still needs restraint, and how to deploy it without losing trust, control, or operational clarity.
Small language models are becoming a practical choice for teams that need fast, private, and efficient AI on phones, laptops, embedded systems, and edge devices. This guide explains where they outperform larger cloud models, where their limits still matter, and how to deploy them responsibly.
Synthetic data is becoming a practical part of modern model training as teams face data scarcity, privacy constraints, and rising demand for domain-specific performance. This guide explains where synthetic data helps, where it fails, and how to use it responsibly in training, fine-tuning, and evaluation without overstating what it can do.
AI is changing cyber defense as fast as it is changing cyber risk. This practical guide explains how organizations can use AI cybersecurity to improve detection, automate response, protect AI-enabled systems, and build a more resilient security program without overpromising what AI can do.
Agentic AI systems are pushing AI beyond passive text and image generation into software workflows, security operations, video simulation, and healthcare support. The real shift is architectural: models are being combined with tools, memory, orchestration, sensors, and governance so they can act, not just respond.
A strong agentic AI business system is built through architecture, not prompt luck. These nine practical tips cover memory, retrieval, tools, guardrails, orchestration, and evaluation.
The most effective business AI agents are not built by giving a model a bigger prompt and hoping for the best. They are assembled as systems: models, tools, orchestration, external memory, retrieval, security controls, and verification loops working together. Here is the high-level architecture that makes modern agentic AI useful in production.