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What We Learned from a Year of Building with LLMs (Part II)

Jun 01, 2024 - oreilly.com
The article discusses the operational aspects of building and managing Large Language Model (LLM) applications. It covers four key areas: data, models, product, and people. For data, it suggests regular review of LLM inputs and outputs and measures to reduce test-prod skew. For models, it advises on integrating language models into the stack, versioning models, and migrating between models. For product, it emphasizes early involvement of design in the application development process, designing user experiences with rich human-in-the-loop feedback, prioritizing requirements, and calibrating product risk. For people, it discusses hiring strategies, fostering a culture of experimentation, and the importance of process over tooling.

The article also highlights the importance of operationalizing LLM applications, which involves addressing questions familiar from traditional software systems and new ones unique to LLMs. It underscores the need for regular data review, structured output for downstream integration, model versioning, and choosing the smallest model that can do the job. It also emphasizes the need for early and frequent design involvement, designing for human-in-the-loop feedback, ruthless prioritization, and risk calibration based on use case. Finally, it advocates for a focus on process over tools, a culture of constant experimentation, and the recognition of the emerging role of AI engineers.

Key takeaways:

  • Operational perspective in AI development involves addressing organizational dysfunction and rising to challenges, focusing on data, models, product, and people.
  • Large Language Models (LLMs) are dynamic and constantly evolving, requiring regular review of data samples and the development of an intuitive understanding of their performance.
  • When working with LLMs, it's important to generate structured output for easy downstream integration, migrate prompts across models, version and pin models, and choose the smallest model that can effectively perform the task.
  • Building AI products should be centered around the job to be done, not the technology that powers them. It's important to involve design early, design UX for Human-in-the-Loop, prioritize requirements ruthlessly, and calibrate risk tolerance based on the use case.
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