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 Corrective RAG - Optimiser les LLM en production training are: Maîtrise avancée de Python, expérience en frameworks LLM comme LangChain ou LlamaIndex, connaissance des bases de RAG et déploiement en production. A preliminary interview validates your eligibility.

Is the training available in person?

Yes, the Formation Corrective RAG - Optimiser les LLM en production training is available in présentiel. The format can be adapted to your needs (inter, intra, custom).

Formation Corrective RAG - Optimiser les LLM en… | 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 Corrective RAG - Optimiser les LLM en 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 Formation Corrective RAG - Optimiser les LLM en 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 Formation Corrective RAG - Optimiser les LLM en 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

Maîtriser les techniques de corrective RAG pour éliminer 90% des hallucinations LLM en formation professionnelle

Développer des pipelines RAG auto-correctifs avec Python et outils experts pour entreprises

Concevoir des systèmes de reranking et self-correction avancés en compétences certifiantes

Implémenter des agents RAG hybrides optimisés pour des réponses précises en production

Optimiser les embeddings et retrieval pour scaler les applications IA professionnelles

Déployer des solutions corrective RAG robustes avec monitoring en temps réel

Évaluer et certifier les performances RAG via métriques expertes en entreprise

Training program

Module 1Diagnostic Corrective RAG : analyse des faiblesses LLM (Python, LangChain, métriques)

Plongez dans le diagnostic avancé des échecs RAG avec Python et LangChain, en identifiant hallucinations et biais via outils comme RAGAS et DeepEval, réalisez des audits concrets sur datasets réels, configurez des métriques personnalisées pour évaluer fidélité et pertinence, construisez un dashboard monitoring initial, appliquez des cas d'entreprise pour transformer les faiblesses en forces correctives, terminez par un livrable : rapport d'audit actionable prêt pour production.

Module 2Implémentation Corrective RAG : self-correction et reranking (LlamaIndex, agents hybrides)

Construisez des pipelines corrective RAG experts en Python avec LlamaIndex et agents autonomes, intégrez self-reflection pour auto-corriger réponses, déployez rerankers comme Cohere ou bge-large pour booster précision de 40%, testez sur scénarios complexes d'entreprise avec vector stores FAISS et Pinecone, optimisez chains multi-hop pour queries imbriquées, simulez charges production via exercices hands-on, générez livrables : code modulaire réutilisable et API corrective RAG fonctionnelle.

Module 3Déploiement et optimisation Corrective RAG : scaling et monitoring (FastAPI, Docker)

Déployez vos systèmes corrective RAG en production avec FastAPI et Docker pour scalabilité infinie, intégrez observabilité via LangSmith et Prometheus pour tracking en temps réel, optimisez coûts embeddings avec batching intelligent et fine-tuning, affrontez challenges edge cases via A/B testing avancé, sécurisez pipelines avec garde-fous éthiques, appliquez études de cas réelles d'entreprises leaders en IA, finalisez par livrables : déploiement cloud-ready, certification compétences et plan d'évolution post-formation.