Use human feedback loops to reinforce learning and build smarter, more reliable AI
AI models often hallucinate and provide inaccurate, biased or misaligned responses and struggle to read user intent & stay relevant as the conversation goes on. HitechDigital combines human common sense with the training process to ensure that models align with what matters to humans.
As a Reinforcement Learning from Human Feedback (RLHF) services company, we create custom datasets for RLHF work, tuning in prompts and training up models with the right reinforcement learning techniques like Proximal Policy Optimization (PPO) and much more. Our RLHF solutions are built to reduce all the fake stuff, make the factual stuff a little more accurate, and get the generative AI working as best as it can.
Our RLHF process starts with making a custom dataset & then adding in human feedback to the reinforcement learning bit. We use raters who specialize in domain, high end annotation platforms, and layers of quality control, all of which we can set up to happen in real time.
Whether you want to fine-tune your LLM or enhance your existing model, our systems & workflows give you all the transparency, compliance, and integration you need to make your AI reinforcement learning development go smoothly.
60 %
fewer hallucinations
250 K+
prompts optimized
2 x
faster fine-tuning loops
75 %
fewer bias flags
Align your AI with trusted RLHF services from HitechDigital.
Leverage Human Feedback Now →Comprehensive RLHF solutions to align AI with your real-world business goals.
We build task-specific RLHF datasets through expert annotation and ranking, ensuring reliable training data to align AI with business expectations and goals.
Our experts rank and score model outputs to create reliable training signals for LLM reinforcement learning and build stronger user-aligned response behavior.
We rewrite and structure prompts to improve model clarity, boost comprehension, and strengthen reinforcement learning outcomes for different user contexts.
Design and execute RLHF AI experiments, benchmark reward strategies, and validate training methods with our tailored, research-led development pipelines.
Close the feedback loop using proven reinforcement learning algorithms like PPO to refine model behavior and performance from human-ranked feedback.
Detect and reduce misleading or false responses with targeted validation cycles designed for RLHF machine learning systems in high-stakes applications.
Model-ready feedback datasets that match task, tone, and domain requirements.
Raters rank outputs with precision to train models aligned with user expectations.
Enhance LLM responses through prompt rewrites to reduce misfires and drift.
Feedback data to improve reward signal reliability and training focus.
Flag false responses quickly with targeted human validation at scale.
Apply proven RLHF loops and update cycles validated by experimental benchmarks.
Reinforcement Learning from Human Feedback (RLHF) is the technique to make the AI models learn from human experience and preferences, instead of algorithmic outputs. Our RLHF process involves ranking model responses and using that feedback to fine tune AI model’s future behavior using reinforcement learning.
In order to improve the accuracy of AI models, RLHF uses human judgements to train the AI models. It also helps the model to choose responses that closely aligned to human expectations; while it omits responses that are less irrelevant, biased, or factually incorrect.
Industries that benefit the most from our RLHF services include healthcare, legal, finance, education, e-commerce, and telecom. We incorporate human feedback in our solutions to improve their AI systems like chatbots, recommendation engines, and diagnostic tools.
Human feedback loops reduce AI bias through continuous monitoring and near real time corrections of AI outputs, empowering the models to identify and mitigate unfair patterns. It also helps to retrain AI models to reinforce ethical, inclusive, and accurate behaviors.
Yes. RLHF can be applied to fine tune large language models (LLMs). In fact, applying RLFH improves LLMs ability to follow instructions and respond safely while staying relevant to domain specific intent.
RLHF actively reduces hallucinations in your AI models by roping in human feedback to correct and refine model outputs. It also teaches the model to give outputs in favor of factually correct responses and staying away from answering when it is uncertain. Our human evaluators rank the responses for accuracy, and the output is used to train the reward model to guide the AI to give more truthful and less fabricated information.
We measure the success and Return on Investment (ROI) of a Reinforcement Learning from Human Feedback (RLHF) using a combination of quantitative technical metrics and qualitative business-focused evaluations. We first establish a performance benchmark of the existing model for that accurate comparison and ROI calculation once the RLHF is done.
The support and customization we offer as part of our RLHF services includes custom dataset creation and annotation, diverse human feedback sources, and model tuning support like prompt optimization and reward model grounding.
