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“There is a large class of problems that are easy to imagine and build demos for, but extremely hard to make products out of. For example, self-driving: It’s easy to demo a car self-driving around a block, but making it into a product takes a decade.” - Karpathy

This write-up is about practical patterns for integrating large language models (LLMs) into systems & products. We’ll build on academic research, industry resources, and practitioner know-how, and distill them into key ideas and practices.

There are seven key patterns. They’re also organised along the spectrum of improving performance vs. reducing cost/risk, and closer to the data vs. closer to the user.

• Evals: To measure performance
• RAG: To add recent, external knowledge
• Fine-tuning: To get better at specific tasks
• Caching: To reduce latency & cost
• Guardrails: To ensure output quality
• Defensive UX: To anticipate & manage errors gracefully
• Collect user feedback: To build our data flywheel