In a world obsessed with the power of Large Language Models (LLMs), this guide takes a refreshingly critical approach by highlighting the implementation pitfalls often overlooked. By offering practical Python examples and open source solutions, it equips engineers and technical product managers with the tools to navigate challenges like handling unstructured output and managing context windows. Delving into topics like structured output, input size limitations, and safety concerns, this guide sheds light on concrete problems and offers battle-tested solutions. Readers will discover surprising insights, such as strategies for output chunking and wrestling with the reality gap of hallucination in LLMs.
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