What We’ve Learned From A Year of Building with LLMs – Applied LLMs

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@artesia summarise the link

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The webpage “What We’ve Learned From A Year of Building with LLMs – Applied LLMs” is a comprehensive guide on constructing products with Large Language Models (LLMs). Over the past year, LLMs have matured to support real-world applications while becoming more cost-effective. The webpage emphasizes that although LLMs are now more accessible due to enhanced APIs, building effective systems is still challenging. Lessons learned are categorized into Tactical, Operational, and Strategic sections:

  1. Tactical: Offers prompting tips, evaluation, and monitoring strategies essential for developers working with LLMs. Emphasizes the importance of retrieval-augmented generation (RAG) and structured input/output to improve performance.

  2. Operational: Focuses on the organizational and daily challenges of deploying products, the importance of data quality, dealing with model versioning, and the need for structured workflows for reliability.

  3. Strategic: Discusses the strategic considerations for building AI products, advocating for not rushing into training models without confirming product-market fit and recommending the use of inference APIs initially. It highlights the importance of building robust systems around models, emphasizing the value in the infrastructure rather than just the models themselves.

The guide also covers more adaptive topics like involving design early, creating human-in-the-loop user experiences, and developing an effective team structure to enhance the operations and utility of LLM-integrated products. It encourages prioritizing efforts towards sustainable systems and warns against over-focusing on model sophistication at the expense of strategic product development.