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The Training Agentic RAG - Developing Precise and Autonomous AIs 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 Agentic RAG - Developing Precise and Autonomous AIs 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 Agentic RAG - Developing Precise and Autonomous AIs 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.
Introduction to the key concepts of Agentic RAG with quick setup of the Python and LangChain environment, creation of a first retriever agent that queries vector databases such as FAISS or Pinecone, practical exercises on integrating external tools to refine queries, development of a complete pipeline on a concrete business case like a Q&A assistant, tests to evaluate the precision of generated responses, production of a functional deliverable ready for deployment with detailed documentation for immediate production rollout.
Target audience
AI developers, data scientists, ML engineers seeking to advance their skills in agentic AI
Prerequisites
Python basics, knowledge of LLMs and vector embeddings
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