Is the training Qualiopi certified?

Yes, Learni is a Qualiopi-certified training organization. This certification guarantees the quality of our courses and enables funding through OPCO and other mechanisms.

How can I fund my training?

Our courses are eligible for OPCO funding. Learni supports you through the funding process. A personalized quote is available upon request.

What are the prerequisites for this training?

The prerequisites for the Training LLM-as-Judge - Accurately Evaluate LLMs in Production training are: Mastery of Python, LLMs like GPT or Llama, fine-tuning and OpenAI/Hugging Face APIs. A preliminary interview validates your eligibility.

Is the training available in person?

Yes, the Training LLM-as-Judge - Accurately Evaluate LLMs in Production training is available in distanciel. The format can be adapted to your needs (inter, intra, custom).

Training LLM-as-Judge - Accurately Evaluate LLMs… | Learni

The story of Learni

Founded by passionate advocates of learning and innovation, Learni set out to make professional training accessible to everyone, everywhere in the world. Our team works in the largest cities such as Paris, Lyon, Marseille, and internationally, to support talents and organizations in their skills development.

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Methods, approaches, and teaching resources

The Training LLM-as-Judge - Accurately Evaluate LLMs in Production training is delivered in-person or remotely (blended-learning, e-learning, virtual classroom, remote in-person). At Learni, a Qualiopi-certified training organization, each program is designed to maximize skills acquisition, regardless of the training mode chosen.

The trainer alternates between demonstrative, interrogative, and active methods (through practical exercises and/or real-world scenarios). This pedagogical approach ensures concrete and directly applicable learning in the workplace.

Teaching resources provided

To ensure the quality of the Training LLM-as-Judge - Accurately Evaluate LLMs in Production training, Learni provides the following teaching resources:

Mac or PC computers, high-speed fiber internet, whiteboard or flipchart, video projector or interactive touchscreen (for remote sessions)
Training environments installed on workstations or accessible online
Course materials, practical exercises, and supplementary resources
Post-training access to materials and teaching resources

For in-house training at a location external to Learni, the client ensures and commits to having all necessary teaching materials (IT equipment, internet connection...) for the proper conduct of the training action in accordance with the prerequisites indicated in the communicated training program.

* contact us for remote feasibility** ratio varies depending on the training followed

Assessment methods

The assessment of skills acquired during the Training LLM-as-Judge - Accurately Evaluate LLMs in Production training is carried out through:

During training: case studies, practical exercises, and professional scenarios
At the end of training: self-assessment questionnaire and skills evaluation by the trainer
After training: training completion certificate detailing acquired skills

Training accessibility

Learni is committed to the accessibility of its professional training programs. All our training programs are accessible to people with disabilities. Our teams are available to adapt teaching methods to your specific needs. Do not hesitate to contact us for any accommodation request.

Training objectives

Master LLM-as-Judge protocols for certified corporate evaluations

Develop automated LLM performance evaluation pipelines

Design advanced prompts optimizing AI judgment reliability

Implement composite metrics for professional benchmarks

Optimize LLM-as-Judge against biases and hallucinations in production contexts

Deploy scalable solutions for massive A/B tests on AI models

Analyze results for competitive gains in artificial intelligence

Training program

Module 1LLM-as-Judge Fundamentals: Protocols and Initial Prompts (GPT-4o, Llama 3)

Immersion in LLM-as-Judge principles through practical exercises on MT-Bench and Arena-Hard datasets, hands-on with pairwise comparison prompts using tools like LangChain and Hugging Face Evaluate, building a first custom judge for your business use cases, manual vs. automated comparative tests, generating initial reports highlighting 5x gains in evaluation speed, collective code review to refine approaches.

Module 2LLM-as-Judge Architecture: Scalable Pipelines (vLLM, Ray Serve)

Design of end-to-end pipelines to evaluate 1000+ responses per hour, integration of vLLM for fast inference and Ray for distribution, exercises on fine-tuning specialized judges for code review or RAG, implementation of multi-step judgment chains reducing errors by 30%, real business cases with live A/B testing, production of interactive dashboards via Streamlit to visualize Spearman correlations.

Module 3LLM-as-Judge Optimization: Bias Mitigation and Robustness (Debias Techniques)

In-depth analysis of positional and length biases using biased datasets, development of debiasing techniques like self-consistency and majority vote, implementation with libraries like EleutherAI and OpenAI Moderation, practical exercises on 50 real hallucination scenarios, calibration of judges for 90%+ human alignment, creation of custom deliverables including audit plans for secure production deployments.

Module 4Advanced LLM-as-Judge Metrics: Hybrid Benchmarks (G-Eval, Auto-J)

Exploration of composite metrics like G-Eval and JudgeLM for granular evaluations, integration with frameworks like DeepEval and RAGAS, collaborative workshops on custom benchmarks for QA and code generation, optimization for >0.85 correlations with human experts, tests on your internal models with automated feedback loops, generation of executive reports demonstrating 70% ROI in evaluation time reduction.

Module 5Production Deployment of LLM-as-Judge: Monitoring and Scaling 2026 (Kubernetes, Prometheus)

Full-stack deployment on Kubernetes with Prometheus/Grafana monitoring for drift detection, CI/CD configuration via GitHub Actions for continuous judges, exercises on scaling to 10k evaluations/day, case studies from leading AI companies, finalization of red thread project with exposed API and complete docs, Q&A session on 2026 strategy integrating multimodality, skill certification via simulated production deployment.