Topics

Practices

• Cost and cachingLLM costs are driven by tokens: input, output (including thinking), and cached input. Most savings come from not reprocessing the same context, choosing the right effort and model for each task,…
• Evaluating LLM applicationsAn eval is a repeatable test of how well a model, prompt, or agent does a task. Without evals, changes to prompts, models, or tools are guesswork. Public benchmarks compare models in general; your…
• Prompting and context engineeringPrompting is writing the instructions and context a model works from. As models have improved, the craft has moved from tricks and magic phrases toward context engineering: choosing what…
• Security for LLM apps and agentsLLM applications add new attack surfaces: the model follows instructions found in any text it reads, and agents can act on those instructions with real tools and credentials. The core rule is to…