Tag: prompt-engineering
Multi-Agent Workflows: Orchestrating Specialized AI Agents
Learn when splitting work across cooperating agents actually pays off, how to coordinate them reliably, and which failure modes will bite you…
Building an Eval Harness for Your AI Features
"It looks good" is not a test. Learn how to build a lightweight evaluation loop that catches prompt regressions, model drift, and…
Structured Outputs and Tool Use: Making LLMs Reliable
Learn how to force valid JSON and well-typed tool calls from Claude so AI output plugs directly into your code, eliminating the…
Cutting LLM Costs with Prompt Caching and Smart Context Management
Prompt caching can dramatically reduce your token spend, but only if your prompts are structured correctly. Here is how prefix-matching works, what…
Choosing the Right Claude Model: Speed, Cost, and Capability Trade-offs
Not every task needs your most powerful model. This guide gives you a practical decision framework for matching Claude model tiers to…
Retrieval-Augmented Generation (RAG): A Practical Build Guide
Learn how to ground an LLM in your own documents using chunking, embeddings, and vector retrieval, so your app returns accurate answers…
Prompt Engineering Patterns That Actually Work
A handful of battle-tested techniques separate prompts that work once from ones you can ship to production. Here is how to use…
Writing Custom Skills to Extend Your AI Coding Assistant
Reusable, file-based skills let you encode your team's workflows and best practices directly into an AI agent, so it stops making the…