Stop guessing. Engineer LLMs like reliable software. | Prompt Engineering for Developers

Most prompt engineering advice is written for marketers, not engineers. This 65-page technical handbook covers what actually matters for production: forcing deterministic JSON from probabilistic models, designing agentic tool-calling loops, building RAG pipelines, and writing eval suites so a prompt tweak doesn't silently break your app. Code examples, architectural patterns, and zero fluff. £29 one-time — the patterns that take months of expensive API calls to figure out on your own.

ElevenAgents by ElevenLabs — Scale conversations without scaling your team

Scale conversations without scaling your team

Hey Product Hunt 👋 I built this because every prompt engineering resource I found was aimed at people writing marketing copy — not developers trying to ship reliable AI features. This guide is purely technical. It covers the things I kept having to figure out the hard way: why structured output guarantees matter, how to design tool schemas a model will actually use correctly, how to chunk documents for RAG without destroying retrieval quality, and how to build eval suites that catch regressions before your users do. It's 65 pages, £29, and written entirely for backend engineers and technical founders — not "AI enthusiasts." Happy to answer any questions about what's inside or dig into any of the topics covered. What's the messiest LLM integration problem you're dealing with right now? 🧵

ElevenAgents by ElevenLabs — Scale conversations without scaling your team

Scale conversations without scaling your team