What We've Learned From A Year of Building with LLMs

The article discusses the potential and challenges of building with large language models (LLMs). It highlights that while LLMs have become increasingly accessible and are predicted to attract significant investment, creating effective AI products remains complex. The authors share lessons and methodologies informed by machine learning that are crucial for developing products based on LLMs. They also provide advice and lessons for anyone building products with LLMs, organized into three sections: Tactical, Operational, and Strategic. The Tactical section covers the basics of working with LLMs, the Operational section discusses the day-to-day concerns and organizational aspects of building with LLMs, and the Strategic section takes a long-term view and considers where the business should invest.

Key takeaways

Large Language Models (LLMs) have become increasingly accessible and are predicted to drive a $200B investment in AI by 2025. However, creating effective AI products beyond a demo remains challenging.
Key lessons for developing products based on LLMs include focusing on fundamental prompting techniques, structuring inputs and outputs, having small prompts that do one thing well, and crafting your context tokens.
Retrieval-augmented generation (RAG) is an effective way to provide knowledge as part of the prompt, improving the LLM's output. The quality of the RAG's output depends on the relevance, density, and detail of the retrieved documents.
Despite the advent of models with large context windows, RAG remains relevant as it provides a way to select information to feed into the model and helps manage costs associated with large context lengths.

What We've Learned From A Year of Building with LLMs

Key takeaways

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