1
Prompt Engineering Best Practices for 2026
System prompts, few-shot examples, chain-of-thought, and structured output — the patterns that turn unreliable LLM experiments into production-ready features.
Issue #3
AI Growth Stack Weekly #3
This week we're going deep on the AI engineering side: prompt engineering patterns that ship reliable features, the fine-tuning decision framework, and how to cut LLM costs by 80% without sacrificing quality.
2
Fine-Tuning vs Prompting: Making the Right Call
90% of teams that jump to fine-tuning should have stuck with prompt engineering. Here's the decision framework, data requirements, and hybrid approaches that work.
3
The LLM Cost Optimization Guide
Model routing, semantic caching, prompt compression, and batch processing — a practical playbook for cutting LLM costs from $9K/month to $500 without losing quality.
4
Conversion Optimization with AI
How AI transforms conversion optimization from incremental button-color tests to systematic journey optimization across your entire funnel.
5
OpenAI Batch API: 50% Cost Reduction
OpenAI's batch API offers 50% off for non-real-time workloads. We explore which growth use cases should migrate to batch processing for massive cost savings.