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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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The Training Self-RAG 2026 - Optimizing Autonomous LLMs in the Enterprise 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.
To ensure the quality of the Training Self-RAG 2026 - Optimizing Autonomous LLMs in the Enterprise training, Learni provides the following 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.
The assessment of skills acquired during the Training Self-RAG 2026 - Optimizing Autonomous LLMs in the Enterprise training is carried out through:
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Learni training programs are available for inter-company and intra-company settings, both in-person and remote. Registration is possible up to 48 business hours before the start of training. Our programs are eligible for OPCO, Pôle emploi, and FNE-Formation funding. Contact us to discuss your training project and funding possibilities.
Dive into setting up a Self-RAG 2026 environment with LangChain and LlamaIndex, configure intelligent retrievers and reflection modules for autonomous decisions, perform practical exercises on real enterprise datasets by correcting hallucinations via self-critique, develop a complete pipeline with fine-tuning on open-source LLMs like Llama 3.1, test in production conditions with precision metrics, produce a cloud-deployable deliverable and document efficiency gains for immediate enterprise application.
Target audience
AI Engineers, data scientists, LLM architects seeking to upskill in Self-RAG for professional projects
Prerequisites
Advanced mastery of classic RAG, expert Python, LangChain/LlamaIndex frameworks, LLMs like Llama 3 or GPT-4o
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