Learn more quickly. Dive deeper. Look further.
It is an exciting time to work with large language models (LLMs). In the last year, LLMs have reached a level where they are considered “good enough” for real-world applications. The rapid advancements in LLMs, along with numerous demonstrations on social media, are expected to drive a $200 billion investment in AI by 2025. LLMs are now widely accessible, allowing not only machine learning engineers and scientists but everyone to incorporate intelligence into their products. While the entry barrier for AI product development has been lowered, creating products that are effective beyond just a demo can still be a challenging task. We have identified some crucial lessons and methodologies, often overlooked, that are essential for building products based on LLMs. Understanding these concepts can give you a competitive edge in the field, even without extensive ML expertise.
In the past year, the six of us have been working on real-world applications using LLMs. We realized the need to compile these lessons for the benefit of the community. Coming from diverse backgrounds and roles, we have all faced the challenges of working with this new technology firsthand. Some of us are independent consultants who have helped clients bring LLM projects from concept to successful products. One of us is a researcher studying ML/AI team dynamics and workflow improvement. Two of us lead applied AI teams at a tech giant and a startup. And one of us has experience teaching deep learning and now focuses on simplifying AI tools and infrastructure. Despite our varied experiences, we have noticed consistent themes in the lessons we’ve learned and are surprised that these insights are not more widely discussed. Our aim is to provide a practical guide to building successful products using LLMs, drawing from our experiences and industry examples.
We have spent the past year gaining valuable insights through hands-on experience and have organized our learnings into three sections: tactical, operational, and strategic. This piece is the first of the three and focuses on the tactical aspects of working with LLMs. We share best practices and common pitfalls related to prompting, retrieval-augmented generation, flow engineering, and evaluation and monitoring. Whether you are a practitioner working with LLMs or a hobbyist exploring weekend projects, this section is tailored for you. Stay tuned for the upcoming operational and strategic sections.
Ready to dive in? Let’s get started.
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