LLMs in Production: A Developer's Complete Prompting Playbook

A hands-on, code-first series that takes you from writing your first zero-shot prompt to shipping production-grade LLM features with typed tool calling, semantic caching, multi-agent orchestration, and automated eval pipelines. Each part builds directly on the last — start at Part 1 and work through, or jump to the technique you need today.

3 posts ~15 min total read Updated April 28, 2026

Posts in this series

1
Part 1 of 3
Zero-Shot to Chain-of-Thought: A Developer's Guide to Writing Effective AI Prompts
Most AI outputs are only as good as the prompt behind them. Learn the four core prompting techniques — with real code examples — to get consistent, high-quality results every time.
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2
Part 2 of 3
Advanced AI Prompting for Developers: JSON Mode, Prompt Chaining, RAG, and Automated Evals
Move beyond basic prompting. Learn how to enforce JSON output, chain prompts into pipelines, integrate RAG, and build automated evals that catch regressions before production.
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3
Part 3 of 3
Beyond Prompt Chaining: Function Calling, Semantic Caching, Multi-Agent Systems, and Eval Datasets
Ship LLMs that don't break in production. Master function calling, semantic caching, multi-agent orchestration, and persistent eval datasets with real Node.js code.
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