The complete prompt evaluation checklist: coverage, scoring, and regression - all in one placeEvery dimension, metric, and failure mode to assess before shipping a prompt to production
A complete prompt evaluation checklist covering test coverage, scoring rubrics, edge case detection, and regression testing for AI systems.
AI Engineering Fundamentals: What It Is, What It Isn't, and Why It's Not Just MLA practical breakdown of AI engineering beyond hype, buzzwords, and academic machine learning
AI engineering is not about training models from scratch. This article clarifies what AI engineering really is, what it is not, and how it differs from data science and traditional machine learning.
The Real Skillset of an AI Engineer: Complementary Skills That Actually MatterWhy systems thinking, software engineering, and product sense beat pure model expertise
AI engineering requires far more than prompt writing or model tuning. Learn the complementary skills AI engineers need, from system design and APIs to observability, security, and cost control.
AI Workflows vs AI Agents: Stop Overengineering Your AI SystemsWhen deterministic pipelines outperform autonomous agents-and when they don't
AI workflows and AI agents solve very different problems. This article breaks down deterministic AI workflows versus non-deterministic AI agents and gives you a clear decision framework to avoid overengineering your AI architecture.
LLM Integrations in Practice: Architecture Patterns, Pitfalls, and Anti-PatternsHow to integrate large language models into real systems without creating fragile, expensive messes
Integrating LLMs into production systems is an engineering problem, not a demo exercise. This post covers proven integration patterns, common mistakes, and what not to build with LLMs.
AI Agents vs AI Pipelines: An Architectural Trade-off, Not a TrendUnderstanding control flow, feedback loops, and failure modes
AI agents are not a silver bullet. This post compares AI pipelines and agent-based systems through an architectural lens, focusing on control flow, failure modes, and long-term maintainability.
Deterministic vs Non-Deterministic Workflows in Screenplay Pattern: A Guide for AI-Powered UI AutomationUnderstanding when to use structured workflows versus adaptive AI-driven approaches in your test automation strategy
Explore the key differences between deterministic and non-deterministic workflows in Screenplay pattern UI automation. Learn how actors use interactions to perform tasks, answer questions, and when to apply each workflow type for optimal test reliability and AI flexibility.
Microservices vs. Monolithic Architecture in AI Agent Systems: A Comprehensive Decision FrameworkChoosing the Right Architectural Pattern for Your Multi-Agent AI Infrastructure
Explore the trade-offs between microservices and monolithic architectures for AI agent systems. This guide provides a practical decision framework with real-world examples, performance benchmarks, and best practices for scaling intelligent agent workflows.
Quickstart: Build a Memory-Enabled AI Assistant with RAG in a WeekendA minimal architecture that scales: ingestion, retrieval, conversation state, and observability.
Follow a practical quickstart to create a memory-enabled AI assistant using RAG, including ingestion, indexing, conversation state, caching, and basic monitoring.