What Are AI Guardrails? Safety Layers for AI Applications
AI guardrails are safety layers that check the input and output of an AI application against your rules. Learn the types, the limits and a practical checklist for a business chatbot.
Blog · Page 58
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What Is a Monorepo? Pros and Cons of Managing Many Projects in One Repo
A monorepo keeps several projects in one version control repository. We explain how it works, how it differs from a polyrepo and when it makes sense for your team.
What Is Fine-Tuning and LoRA? A Practical Guide to Tuning AI Models
Fine-tuning and LoRA in plain language: how they work, when prompting or RAG is enough, how to prepare data, which risks matter, and what to check before you start.
What Is Temperature and Top-p in AI? The Creativity Setting Explained
Temperature and top-p control how random a language model is when it picks the next word. Learn the concepts, the differences, common mistakes, and a simple testing plan.
What Is Synthetic Data? How AI Generated Data Is Used
Synthetic data is artificial data that mimics real records. We explain how it is generated, where it helps, which risks it carries, and how it differs from real and anonymized data.
What Is a Reasoning Model? How It Differs from a Classic LLM
A reasoning model is an AI model that generates hidden thinking steps before it answers. Learn how it works, how it differs from a classic LLM, and when it is worth the cost.
What Is Observability? OpenTelemetry, Logs, Metrics and Traces
What is observability, and how does it differ from monitoring? We explain logs, metrics, traces, why OpenTelemetry is vendor-neutral, the collector, and a starter checklist for small teams.
What Is Vibe Coding? Opportunities and Risks of Writing Code With AI
Vibe coding means describing software in plain language and steering an AI model's code by testing it. Learn where it helps, which risks it carries, and what to check before production.
What Is Knowledge Distillation? How a Small AI Model Learns From a Big One
Knowledge distillation teaches a small AI model to imitate a large one. Learn how it works, how it differs from quantization and pruning, and what risks to check.
What Is Mixture of Experts (MoE)? How the Architecture Works
Mixture of experts (MoE) explained at concept level: the router, sparse activation, the gap between total and active parameters, limits, and how MoE compares with dense models.