Unlock the future of LLM development with Honeycomb
Solve user experience issues in LLM-based applications with real-world usage data. Generative AI introduces powerful, but often unpredictable, new experiences to your users. Analyze accuracy, quality, and performance – and dive deep into your LLM’s execution – to drive continuous improvement.
Free Download: Observability for Large Language Models
The definitive playbook on understanding and improving your use of LLMs written by Honeycomb’s own Phillip Carter. Observability for Large Language Models shows you how to build a production feedback loop into your LLM development cycle to accelerate refinements and make your product successful.
Real insights for LLM development
Leverage OpenTelemetry and Honeycomb’s observability to gather insights into user behavior, system performance, and user feedback. This data is a foundation for a fast feedback loop driven by detailed behavior from real users. Improve evaluation models for LLM-based software and refine prompts systematically to ensure reliability and prevent regressions.
All the Hard Stuff Nobody Talks About When Building Product with LLMs
LLMs are incredibly powerful. When building Query Assistant, we also learned
they’re non-deterministic black boxes that people use in ways that you can’t hope to predict
upfront. Even subtle changes to a prompt sent to an LLM can result in dramatically different
behavior. Here’s a candid look at how we tackled that.
LLMs Demand Observability-Driven Development
Generative AI is forcing engineers to re-evaluate traditional software
engineering practices – pushing us to embrace observability-driven development and a “ship to
learn” mentality. This paradigm shift benefits all aspects of software development and
engineering, demanding the adoption of modern practices that are no longer optional.