I've spent weeks curating technical implementation details of how companies are actually deploying LLMs and Generative AI in production. The database now contains over 300 case studies with detailed technical summaries (230,000+ words) focusing exclusively on architectural decisions, deployment patterns, and real engineering challenges.<p>Key features:
* Each case study is technically focused - no marketing fluff
* 150+ entries from technical conference talks and panels (saving you 100+ hours of video watching)
* Sophisticated filtering by technical stack, RAG implementations, monitoring solutions etc.
* Summaries generated consistently using Claude for quick insight extraction
* All sources remain public and linked for deeper exploration<p>Some unique insights we've found:
* Common patterns in LangChain production deployments
* Real-world RAG implementation approaches
* Emerging best practices in LLM monitoring
* Novel solutions to prompt engineering workflows
* Production-tested security measures<p>It's a lot to read so we wrote a blog post summarising the main takeaways here <a href="https://www.zenml.io/blog/llmops-lessons-learned-navigating-the-wild-west-of-production-llms" rel="nofollow">https://www.zenml.io/blog/llmops-lessons-learned-navigating-...</a><p>The database is free and designed to help engineering teams learn from others' practical experiences deploying LLMs. I'm particularly interested in hearing about:
1. What specific implementation patterns you'd like to see analyzed (contribute more case studies via the link on the database's main page)
2. Additional case studies you think should be included
3. How you're handling non-deterministic outputs in production<p>Looking forward to your feedback and contributions!