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The Training: Leveraging Your Enterprise Data with LlamaIndex: Complete Guide to Integrating Generative AI 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: Leveraging Your Enterprise Data with LlamaIndex: Complete Guide to Integrating Generative AI 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: Leveraging Your Enterprise Data with LlamaIndex: Complete Guide to Integrating Generative AI 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.
Presentation of the challenges of AI-assisted information retrieval, introduction to LLMs and the LlamaIndex framework, comparison with other tools (LangChain...). Installation, exploration of the Python API and documentation, first simple processing examples.
Study of native LlamaIndex connectors (PDF files, HTML, SQL databases, APIs, Notion, etc.), management of structured and unstructured data, preprocessing best practices, intelligent document segmentation, construction of custom indexing pipelines, deduplication, corpus audit.
Implementation of Retrieval Augmented Generation (RAG), fine-tuning configuration of LlamaIndex with open-source and cloud models, securing document access, evaluation and optimization of responses, building a document chatbot, deployment of a web QA (question-answering) application for the enterprise.
Target audience
Developers, data analysts, technical project managers wishing to integrate an AI layer into document access
Prerequisites
Basic knowledge of Python and fundamental concepts of language models (LLMs)
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