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Evals are the critical building block to achieve magical production ready AI products. HoneyHive powers that - congrats on the progress Mohak and Dhruv

Excited to back the HoneyHive team as they solve one of the most under-appreciated challenges in AI: making systems reliable, measurable, and production-ready💪

Hard to debug & optimize your RAG apps in production? 🤔 LanceDB <> HoneyHive integration gives you the visibility you need. A simple `@ trace` decorator automatically provides detailed telemetry, giving you visibility into pipeline execution, bottlenecks, quality &

By popular demand, we’re excited to introduce an integration with Qdrant! Retrieval powers most AI apps today, but it's so hard to get right. From simple KNN to agentic RAG, how do you know what search strategy works best for your use case? Enter HoneyHive + Qdrant 🤝

🔎 See inside your RAG pipeline with Qdrant + HoneyHive You can now trace every step of retrieval and generation: embedding, search, insertion, and LLM response. With 🐝 HoneyHive you can • Monitor latency and context relevance • Tune chunk size, overlap, and filters •

At HoneyHive, we call them the "YOLO to prod" people. Too easy to ship a half-baked app when no one knows how to really do ML The core issue is SWE doesn't prep you for ML's experimentation culture. ML is more science than code—and that's what most people are getting wrong

Funny how this is being resurfaced 4yrs later after Dhruv Singh built the original version at Microsoft: github.com/microsoft/Code…

Because we struggle even with a single agent, never mind the exponential complexity of n agents.

Evals are a means to an end (better product), not the end itself

Most recommendation systems just show you more of what you already like. The trouble is, we can't always describe the new things we might enjoy. This technique works differently. It learns what you like and don't like, then helps you discover truly new content.

Any product designers / people with good UX taste up for grading some UX mockups for a vibe coding eval? 💴 Paid opportunity!

Particularly proud to share that a major Fortune 100 enterprise is already logging their PHI data to our HIPAA-compliant cloud. Learn more about our security policies here: app.drata.com/trust/9cc7ede3…

Chasing benchmark leaderboards is the easiest way to build an AI product that fails in the real world. Most teams waste 90% of their eval effort on academic benchmarks instead of finding exactly where their system breaks with real users.

Introducing Role-Based Access Control (RBAC) in @HoneyHiveAI! Built in partnership with our largest financial services and insurance customers, RBAC brings enterprise-grade security and access controls to your critical observability workflows.