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 Formation LLM-as-judge - Automatiser l'évaluation des outputs IA training are: Bases en Python, notions de LLMs comme GPT ou Llama, utilisation d'API OpenAI. A preliminary interview validates your eligibility.

Is the training available in person?

Yes, the Formation LLM-as-judge - Automatiser l'évaluation des outputs IA training is available in présentiel. The format can be adapted to your needs (inter, intra, custom).

Formation LLM-as-judge - Automatiser l'évaluatio... | 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 Formation LLM-as-judge - Automatiser l'évaluation des outputs IA 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 Formation LLM-as-judge - Automatiser l'évaluation des outputs IA 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 Formation LLM-as-judge - Automatiser l'évaluation des outputs IA 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

Maîtriser les fondamentaux du LLM-as-judge pour évaluations professionnelles

Développer des prompts efficaces en formation certifiante LLM-as-judge

Configurer des pipelines d'évaluation automatisée avec compétences entreprise

Implémenter des métriques de jugement pour outputs IA fiables

Analyser les résultats et biais dans contextes professionnels

Optimiser les setups LLM-as-judge pour projets réels en entreprise

Acquérir des compétences certifiantes en automatisation IA

Training program

Module 1fondamentaux llm-as-judge et installation

Concepts clés LLM-as-judge : rôle des LLMs comme juges. Architecture et cas d'usage professionnels. Installation OpenAI API et Hugging Face. Premiers tests d'évaluation simple. Exercices sur outputs basiques.

Module 2conception prompts llm-as-judge

Règles pour prompts efficaces en LLM-as-judge. Techniques de chain-of-thought prompting. Évaluation de réponses textuelles. Tests comparatifs manuels vs automatisés. Pratique sur datasets publics.

Module 3pipelines llm-as-judge basiques

Construction de pipelines Python avec LangChain. Intégration LLM-as-judge à workflows. Gestion des scores agrégés. Évaluation multi-juges. Debug de prompts défaillants.

Module 4métriques et analyse llm-as-judge

Définition de métriques : similarité, cohérence, factualité. Analyse de corrélations avec juges humains. Détection de biais dans jugements LLM. Visualisation résultats avec Matplotlib. Cas d'erreurs courantes.

Module 5optimisation et déploiement llm-as-judge

Fine-tuning prompts pour précision accrue. Scalabilité en entreprise. Déploiement via Docker. Projet fil rouge : évaluation complète d'un modèle. Bonnes pratiques production.