# Introducing Human-in-the-loop Evaluation for Agentic AI Observability

## Guide

### [The 5 Metrics That Actually Move AI Into Production](https://humansignal.com/blog/the-5-metrics-that-actually-move-ai-into-production)
A practical framework for turning AI metrics into clearer decisions about what to fix, ship, and scale.

### [Scaling AI Data Quality: Best Practices for Onboarding and Evaluating Annotators](https://humansignal.com/blog/scaling-ai-data-quality-best-practices-for-onboarding-and-evaluating-annotators)
A practical workflow for onboarding and evaluating annotators with clear instructions, calibration, quality gates, reviewer feedback, and dashboards that keep labeling consistent as volume grows.

### [How to Scale Evaluation for RAG and Agent Workflows](https://humansignal.com/blog/how-to-scale-evaluation-for-rag-and-agent-workflows)
A practical workflow for evaluating RAG and agent systems consistently through prompt, model, and workflow changes.

### [How Legalbenchmarks.ai Built a Domain-Specific AI Benchmark](https://humansignal.com/blog/how-legalbenchmarks-ai-built-a-domain-specific-ai-benchmark)
See how Legalbenchmarks.ai built a custom contract-drafting AI benchmark using an SME-first, human-in-the-loop framework with rubric-based scoring and LLM judges.

### [SAM2 vs YOLO for Bounding Box Labeling: Which Should You Use?](https://humansignal.com/blog/sam2-vs-yolo-for-bounding-box-labeling-which-should-you-use)
A practical comparison of SAM2 and YOLO for bounding box labeling, with pros, cons, and how to use each model in Label Studio.

### [9 Criteria for Successful AI Projects](https://humansignal.com/blog/9-criteria-for-successful-ai-projects)
Most AI projects fail from unclear goals and poor alignment. These nine criteria show how to build systems that scale, adapt, and deliver value.

### [The Virtuous Cycle of Using Your Brain Alongside GenAI - A Case Study](https://humansignal.com/blog/the-virtuous-cycle-of-using-your-brain-alongside-genai-a-case-study)
This case study shows how human oversight turns AI missteps into opportunities for better systems, documentation, and long-term reliability.

### [Operationalizing Compliance with the EU AI Act’s High-Risk Requirements](https://humansignal.com/blog/operationalizing-compliance-with-the-eu-ai-act-s-high-risk-requirements)
The EU AI Act brings enforceable demands for transparency, oversight, and auditability, this guide shows how AI teams can meet them through robust infrastructure.

### [What Is a Spectrogram? Understanding Sound Beyond the Waveform](https://humansignal.com/blog/what-is-a-spectrogram-understanding-sound-beyond-the-waveform)
In this post, we explore how spectrograms work and why they’re essential for analyzing speech, music, and machine learning audio data.

### [Intro to Hidden Markov Models: When the State Is Hidden but the Signal Isn't](https://humansignal.com/blog/intro-to-hidden-markov-models)
This blog introduces the core components and algorithms behind HMMs, from decoding hidden states to calculating sequence likelihoods.

### [Data Labeling with GPT-4 in Label Studio: ML Backend Integration](https://humansignal.com/blog/data-labeling-with-gpt-4-in-label-studio-ml-backend-integration)
This blog shows how to integrate GPT-4 with Label Studio to shift from manual labeling to faster label review. Use OpenAI’s API and the ML Backend to streamline text annotation and accelerate NLP workflows.

### [How to Label Spectrograms for AI Models](https://humansignal.com/blog/how-to-label-spectrograms-for-ai-models)
You can now label audio data for training or fine-tuning AI models using spectrograms. This blog outlines why you might use spectrograms and how to use them in Label Studio.

### [Markov Models: Chains to Choices](https://humansignal.com/blog/markov-models-chains-to-choices)
This post breaks down Markov Chains and Markov Decision Processes with intuitive examples and a video walkthrough.

### [Your Data Engine Is the Moat – Here’s How to Own It.](https://humansignal.com/blog/your-data-engine-is-the-moat-here-s-how-to-own-it)
Model weights can be rented, but your feedback loop is the moat. This blog explains how leading AI teams are reclaiming control over labeling, evaluation, and iteration, without sacrificing speed.

### [The Rise of Real-World Robotics—and the Data Behind It](https://humansignal.com/blog/the-rise-of-real-world-robotics-and-the-data-behind-it)
Robots need labeled data to navigate the real world. This blog explores why robotics annotation is uniquely complex and how teams can get it right.