Workflow · Production

Humanloop: Foundation Model Ops platform for prompt management and LLM evaluation

The problem

AI engineers building LLM applications face a fragmented toolkit — prompt sharing, versioning, evals, monitoring, and finetuning all require cobbled-together solutions — and closed-source LLM APIs change unpredictably, making it hard to detect quality regressions in production.

First attempt

Humanloop's original automated labeling product for NLP was abandoned after InstructGPT made clear that the market for annotated data labeling was heading into freefall.

Workflow diagram · grounded in source
1
AI app ships to production
trigger
“shipping a quick and easy demo, and then having to cobble together a bunch of solutions for prompt sharing and versioning, running prompt evals and monitoring, storing data and finetuning as their AI apps go from playground to production”
2
Prompt quality monitoring
validation
“the only recourse is to build or buy a Foundation Model Ops platform that can help you understand if your prompt results are declining in quality based on your own evaluations and your user feedback”
3
LLM-based evaluators on samples
ai_action
“Humanloop has now added an Evaluators feature, which lets you write code or use LLMs to run evals on random samples of your Humanloop workloads and track regressions and improvements over time”
Reported outcome

Humanloop pivoted to a Foundation Model Ops platform for AI engineers, adding an Evaluators feature that uses code or LLMs to run evals on workload samples and track regressions over time.

Reported stack
Humanloop
Source
https://www.latent.space/p/humanloop
Read source ↗

Frequently asked questions

What did this team achieve with this AI workflow?

Humanloop pivoted to a Foundation Model Ops platform for AI engineers, adding an Evaluators feature that uses code or LLMs to run evals on workload samples and track regressions over time.

What tools did this team use?

Humanloop.

What failed first in this deployment?

Humanloop's original automated labeling product for NLP was abandoned after InstructGPT made clear that the market for annotated data labeling was heading into freefall.

How is this workflow AI workflow structured?

AI app ships to production → Prompt quality monitoring → LLM-based evaluators on samples.