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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 Corrective RAG - Ensuring Reliable AI Responses 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.
To ensure the quality of the Training Corrective RAG - Ensuring Reliable AI Responses in Production 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 Corrective RAG - Ensuring Reliable AI Responses in Production training is carried out through:
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.
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.
Analysis of classic weaknesses in RAG pipelines. Identification of hallucinations and inaccuracies. Setting up a test environment with LangChain and LlamaIndex. Exercises on concrete enterprise cases: diagnosis of a failing RAG. Introduction to self-reflection techniques. Creation of the ongoing project: a RAG chatbot for customer support. Hands-on with advanced vector stores. Initial precision tests. (112 words)
Development of hybrid reranking modules. Integration of self-correction with critical LLMs. Professional exercises on iterative feedback loops. Optimization of prompts for automatic correction. Real cases: real-time correction for enterprise Q&A. Hands-on with ongoing project using Pinecone and GPT-4. Evaluation of metrics: faithfulness, answer relevance. Advanced debugging of retrieval failures. Integration of guardrails for robustness. (98 words)
Scalability of Corrective RAG pipelines with Docker and Kubernetes. Live performance monitoring. Exercises on A/B testing and fine-tuning. Secure deployment for enterprise environments. Finalization of the ongoing project: full-stack deployment of a corrective RAG. Certifying evaluation with business metrics. Best practices for post-deployment maintenance. Hands-on with real cases: RAG for internal search. Support for Qualiopi certification. (92 words)
Target audience
AI Engineers, Data Scientists, LLM Developers seeking to enhance RAG system reliability in enterprise settings for professional development
Prerequisites
Advanced Python proficiency, RAG fundamentals, LLM usage (GPT, Llama), embeddings and vector stores such as Pinecone or FAISS
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