Human-in-the-Loop AI

User Stories

As an AI researcher, I want to implement RLHF so I can align language model outputs with human preferences
As a data scientist, I want active learning so I can minimize labeling costs while maximizing model performance
As a product manager, I want human feedback integration so I can continuously improve AI quality based on user interactions
As an annotation team lead, I want quality assurance workflows so I can ensure consistent, high-quality training data
As an ML engineer, I want DPO implementation so I can align models without complex reward model training

Industry Applications

AI Assistants

Chatbot alignment, helpfulness optimization, safety fine-tuning

Content Moderation

Policy alignment, edge case labeling, appeal review systems

Healthcare

Clinical annotation, diagnostic feedback, treatment recommendation validation

Legal

Document review, contract analysis, case classification

Autonomous Vehicles

Edge case labeling, scenario annotation, safety validation

Implementation Steps

Step 01

Feedback Collection

Design interfaces for collecting human preferences, comparisons, and corrections

Step 02

Annotation Pipeline

Build annotation workflows with quality checks, gold standards, and inter-annotator agreement

Step 03

Active Learning

Implement uncertainty sampling to prioritize most informative examples for labeling

Step 04

Reward Modeling

Train reward models from preference data (RLHF) or use direct optimization (DPO)

Step 05

Policy Training

Fine-tune models using PPO (RLHF) or direct preference optimization algorithms

Step 06

Continuous Feedback

Deploy feedback loops for ongoing model improvement from production interactions

Core Components

Component	Function	Tools
RLHF Pipeline	Reward model training, PPO policy optimization	TRL, TRLX, DeepSpeed-Chat
DPO Training	Direct preference optimization without reward models	TRL DPOTrainer, Axolotl
Active Learning	Uncertainty sampling, query-by-committee, diversity sampling	modAL, ALiPy, Cleanlab
Annotation Platform	Task design, annotator management, quality assurance	Label Studio, Scale AI, Labelbox
Quality Assurance	Inter-annotator agreement, gold standard checks	Custom pipelines, Fleiss' Kappa
Feedback Integration	Production feedback collection and incorporation	Custom APIs, MLflow