How MasterClass uses Collinear data packs to build authentic AI instructor Personas
"Collinear AI’s groundbreaking work using Knowledge Infusion and Auto-Align has been instrumental in making our AI models reliable and safe. This innovative approach has helped us strike an ideal balance between conversation quality and safety, while also enabling quicker, iterative improvements compared to traditional fine-tuning methods. With Collinear, we’ve significantly accelerated our AI development process while maintaining the high standards of expertise and inspiration that MasterClass is known for."
MasterClass has redefined personalized learning by giving users direct access to the world’s best minds, from award-winning chefs to legendary negotiators. As they expanded into AI-powered instruction through MasterClass On Call, they set out to build AI personas that could authentically reflect each instructor’s voice, teaching style, and expertise — without compromising trust.
But the stakes were high. A single hallucinated anecdote, fabricated FBI story, or off-brand coaching tactic could harm both user trust and the professional reputation of instructors who had spent decades shaping their craft. Maintaining authenticity was, and is, the core product promise.
The Challenge: Preserving Authentic Instructor Personas at Scale
To build AI versions of world-class experts, MasterClass needed models that could not only maintain high correctness but also faithfully reproduce each instructor’s unique teaching approach.
However, traditional methods quickly broke down:
- Insufficient instructor-specific data for supervised fine-tuning
- RAG pipelines that improved factuality but lost teaching style
- Generic safety filters rejecting valid instructor-style responses
- High risk of hallucinating cases, personal stories, or experiences
No scalable way to replicate this for all 180+ MasterClass instructors
Representing Chris Voss, whose style includes tactical empathy, calibrated questions, and hostage-negotiation-inspired tactics, made the limitations even more obvious. The models needed persona-specific training data that simply did not exist.
MasterClass needed a way to generate high-quality synthetic data and alignment signals that encoded exactly what made each instructor unique.
"Collinear AI’s groundbreaking work using Knowledge Infusion and Auto-Align has been instrumental in making our AI models reliable and safe. This innovative approach has helped us strike an ideal balance between conversation quality and safety, while also enabling quicker, iterative improvements compared to traditional fine-tuning methods. With Collinear, we’ve significantly accelerated our AI development process while maintaining the high standards of expertise and inspiration that MasterClass is known for."
The Solution: Custom Judges + Persona-specific Data Packs
MasterClass partnered with Collinear to build a complete data pipeline using three components of the Data Engine:
- Custom Quality Judges (Persona-Specific Evaluators): Collinear trained small, specialized judges on just 25–30 examples per instructor to evaluate responses using persona-specific criteria, ensuring the model captured Chris Voss’s calibrated questions, negotiation terminology, tone, and pacing.
- Knowledge Infusion Datasets: To reduce hallucinations and encode instructor knowledge, Collinear generated persona-specific pretraining datasets. High-rank LoRA adapters allowed the model to “memorize” relevant instructional content, outperforming RAG on hallucination benchmarks.
- Alignment Fine-tuning with Judge-generated Preference Pairs: Using the custom judges, MasterClass created high-quality alignment pairs capturing the nuances of each instructor’s teaching style. This produced curated, persona-specific data packs without expensive human annotation, enabling alignment that balanced accuracy, safety, and authenticity.
The Results: Authenticity, Safety, and Measurable Model Gains
The results weren’t just solid — they elevated MasterClass’s entire approach to AI. Here’s what our approach actually delivered:
- Authenticity that users noticed: "The user feedback from our open beta has been extremely positive, and many users directly acknowledged the authenticity of the instructor conversation experience," Aman shares.
- Significant safety and reliability improvements: The custom judges for safety and reliability showed extremely high alignment with MasterClass's policies, allowing them to filter out inappropriate content while preserving the instructors' unique voices.
- Measurable performance gains: "Combining that data and Collinear's AutoAlign training process, we were able to drive over 15% improvement in safety and reliability scores compared to only doing supervised fine-tuning," notes Aman.
- A repeatable recipe for growth: "This collaboration helped us bridge the gap between an alpha version and a production-ready experience. It also gave us a repeatable recipe to build new instructor models that are well aligned and also potentially new future AI experiences," Aman explained.
Need high-quality training data? Discover how Collinear’s curated data packs accelerate post-training alignment.
Stop guessing if your data is good enough for production. Book a demo to see how Collinear builds high-signal, multilingual data packs tailored to your models and domains.
"Collinear AI’s groundbreaking work using Knowledge Infusion and Auto-Align has been instrumental in making our AI models reliable and safe. This innovative approach has helped us strike an ideal balance between conversation quality and safety, while also enabling quicker, iterative improvements compared to traditional fine-tuning methods. With Collinear, we’ve significantly accelerated our AI development process while maintaining the high standards of expertise and inspiration that MasterClass is known for."
MasterClass is a premier online education platform offering cinematic, instructor-led courses from world-renowned experts like Gordon Ramsay, Anna Wintour, Serena Williams, and Chris Voss. Their mission is to bring world-class expertise to every learner through immersive, high-quality instruction.
MasterClass has redefined personalized learning by giving users direct access to the world’s best minds, from award-winning chefs to legendary negotiators. As they expanded into AI-powered instruction through MasterClass On Call, they set out to build AI personas that could authentically reflect each instructor’s voice, teaching style, and expertise — without compromising trust.
But the stakes were high. A single hallucinated anecdote, fabricated FBI story, or off-brand coaching tactic could harm both user trust and the professional reputation of instructors who had spent decades shaping their craft. Maintaining authenticity was, and is, the core product promise.
The Challenge: Preserving Authentic Instructor Personas at Scale
To build AI versions of world-class experts, MasterClass needed models that could not only maintain high correctness but also faithfully reproduce each instructor’s unique teaching approach.
However, traditional methods quickly broke down:
- Insufficient instructor-specific data for supervised fine-tuning
- RAG pipelines that improved factuality but lost teaching style
- Generic safety filters rejecting valid instructor-style responses
- High risk of hallucinating cases, personal stories, or experiences
No scalable way to replicate this for all 180+ MasterClass instructors
Representing Chris Voss, whose style includes tactical empathy, calibrated questions, and hostage-negotiation-inspired tactics, made the limitations even more obvious. The models needed persona-specific training data that simply did not exist.
MasterClass needed a way to generate high-quality synthetic data and alignment signals that encoded exactly what made each instructor unique.
"Collinear AI’s groundbreaking work using Knowledge Infusion and Auto-Align has been instrumental in making our AI models reliable and safe. This innovative approach has helped us strike an ideal balance between conversation quality and safety, while also enabling quicker, iterative improvements compared to traditional fine-tuning methods. With Collinear, we’ve significantly accelerated our AI development process while maintaining the high standards of expertise and inspiration that MasterClass is known for."
The Solution: Custom Judges + Persona-specific Data Packs
MasterClass partnered with Collinear to build a complete data pipeline using three components of the Data Engine:
- Custom Quality Judges (Persona-Specific Evaluators): Collinear trained small, specialized judges on just 25–30 examples per instructor to evaluate responses using persona-specific criteria, ensuring the model captured Chris Voss’s calibrated questions, negotiation terminology, tone, and pacing.
- Knowledge Infusion Datasets: To reduce hallucinations and encode instructor knowledge, Collinear generated persona-specific pretraining datasets. High-rank LoRA adapters allowed the model to “memorize” relevant instructional content, outperforming RAG on hallucination benchmarks.
- Alignment Fine-tuning with Judge-generated Preference Pairs: Using the custom judges, MasterClass created high-quality alignment pairs capturing the nuances of each instructor’s teaching style. This produced curated, persona-specific data packs without expensive human annotation, enabling alignment that balanced accuracy, safety, and authenticity.
The Results: Authenticity, Safety, and Measurable Model Gains
The results weren’t just solid — they elevated MasterClass’s entire approach to AI. Here’s what our approach actually delivered:
- Authenticity that users noticed: "The user feedback from our open beta has been extremely positive, and many users directly acknowledged the authenticity of the instructor conversation experience," Aman shares.
- Significant safety and reliability improvements: The custom judges for safety and reliability showed extremely high alignment with MasterClass's policies, allowing them to filter out inappropriate content while preserving the instructors' unique voices.
- Measurable performance gains: "Combining that data and Collinear's AutoAlign training process, we were able to drive over 15% improvement in safety and reliability scores compared to only doing supervised fine-tuning," notes Aman.
- A repeatable recipe for growth: "This collaboration helped us bridge the gap between an alpha version and a production-ready experience. It also gave us a repeatable recipe to build new instructor models that are well aligned and also potentially new future AI experiences," Aman explained.
Need high-quality training data? Discover how Collinear’s curated data packs accelerate post-training alignment.
Stop guessing if your data is good enough for production. Book a demo to see how Collinear builds high-signal, multilingual data packs tailored to your models and domains.
"Collinear AI’s groundbreaking work using Knowledge Infusion and Auto-Align has been instrumental in making our AI models reliable and safe. This innovative approach has helped us strike an ideal balance between conversation quality and safety, while also enabling quicker, iterative improvements compared to traditional fine-tuning methods. With Collinear, we’ve significantly accelerated our AI development process while maintaining the high standards of expertise and inspiration that MasterClass is known for."
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