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

May 29, 2024 - oreilly.com
The article discusses the potential and challenges of building with large language models (LLMs). It highlights the increasing investment in AI and the accessibility of LLMs for everyone, not just machine learning engineers and scientists. However, it also emphasizes the difficulties in creating effective AI products beyond a demo. The authors share their experiences and lessons learned from building real-world applications with LLMs, including the importance of understanding and applying machine learning concepts for a competitive advantage. They also discuss the need for structured input and output, the use of retrieval-augmented generation (RAG), and the benefits of deterministic workflows.

The authors argue that despite the hype around long-context models, RAG remains a crucial tool for LLMs. They also suggest that breaking down complex tasks into simpler ones and using workflows can improve the performance of LLMs. The article concludes by emphasizing the importance of continuous learning and experimentation in the rapidly evolving field of AI and LLMs.

Key takeaways:

  • Large Language Models (LLMs) have become increasingly accessible and are expected to fuel an estimated $200B investment in AI by 2025. However, creating effective AI products beyond a demo remains a challenging task.
  • Key methodologies for developing products based on LLMs include tactical, operational, and strategic approaches. The tactical approach involves understanding the nuts and bolts of working with LLMs, including best practices and common pitfalls.
  • Retrieval-augmented generation (RAG) is an effective way to provide knowledge as part of the prompt, grounding the LLM on the provided context. The quality of RAG’s output is dependent on the quality of retrieved documents.
  • Optimizing LLM workflows involves breaking down complex tasks into simpler ones, and considering when finetuning or caching can help increase performance and reduce latency/cost. Deterministic workflows are currently preferred for their predictability and reliability.
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