What We Learned from a Year of Building with LLMs (Part I) – O’Reilly
Join the O'Reilly online learning platform. Get a free trial today and find answers on the fly, or master something new and useful. Tracking need-to-know trends at the intersection of business and technology. Please read our privacy policy. To hear directly from the authors on this topic, sign up for the upcoming virtual event on June 20th. Join the O'Reilly online learning platform. Get a free trial today and find answers on the fly, or master something new and useful. It’s an exciting time to build with large language models (LLMs). Over the past year, LLMs have become “good enough” for real-world applications. The pace of improvements in LLMs, coupled with a parade of demos on social media, will fuel an estimated $200B investment in AI by 2025. LLMs are also broadly accessible, allowing everyone, not just ML engineers and scientists, to build intelligence into their products. While the barrier to entry for building AI products has been lowered, creating those effective beyond a demo
What We Learned from a Year of Building with LLMs (Part I) – O’Reilly Skip to main content Search for books, courses, events, and more Toggle dark mode AI & ML Business Data Innovation Research Security Try the O’Reilly learning platform With the O’Reilly learning platform, you get the resources and guidance to keep your skills sharp and stay ahead. Try it free for up to 14 days. Start trial Try a course for free Join a live online event on the O’Reilly platform to learn from the experts shaping tech. See what’s coming soon Get the Radar Trends newsletter Your email Country - Select country -
Explore this link on the map →related reading
- Patterns for Building LLM-based Systems & Productseugeneyan.com
- What We’ve Learned From A Year of Building with LLMs – Applied LLMsapplied-llms.org
- LLM Powered Autonomous Agents | Lil'Loglilianweng.github.io
- LLM Evaluation doesn't need to be complicatedphilschmid.de
- The bitter lesson of LLM evalsparsed.com
- Your AI Product Needs Evals – Hamel's Blog - Hamel Husainhamel.dev
- What We Learned from a Year of Building with LLMs (Part II) – O’Reillyoreilly.com
- GenAI Handbookgenai-handbook.github.io
- Building LLM applications for productionhuyenchip.com
- Guardian Angels: LLM Personalization for Productivity and Security · Gwern.netgwern.net
- Building Effective AI Agents \ Anthropicanthropic.com
- Building Effective AI Agents \ Anthropicanthropic.com