Written after the deployment, not before.

Learn a systematic framework for prompt engineering to move from ambiguous requests to precise, reliable AI outputs. This guide covers the core principles, a step-by-step workflow, and the critical trade-offs involved.

Multi-agent systems add cost and complexity, but are justified for specific use cases like independent verification (debate) or breaking down tasks that exceed a single model's context window (decomposition). This article provides a framework for deciding when to add a second agent.

A major new foundation model release is not a simple drop-in upgrade. This guide provides a framework for migrating your production pipeline safely, covering re-qualification, prompt auditing, and sequenced rollouts to avoid breaking changes.

Improving the safety and helpfulness of large language models often comes at a cost to their raw capabilities. This trade-off, known as the 'alignment tax,' is a fundamental challenge in making AI systems that are both powerful and beneficial.

Large Language Models are powerful but often the wrong tool for the job. This article provides a five-point decision test to identify when a simpler, more reliable solution like a database query, a regular expression, or a classical classifier is the better choice.

Stop creating siloed "AI teams" that become bottlenecks. To scale LLM development, treat AI capabilities as a product built by your platform team, enabling product teams to innovate faster and more safely.

A single powerful LLM is often the most expensive and least reliable way to answer simple questions. This article explains how to build a routing layer that uses deterministic rules for simple queries, saving money and improving reliability, while reserving expensive models for the complex tasks they were built for.