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.

What is the duration of the Training LlamaIndex - Building Powerful RAGs for AI training?

The Training LlamaIndex - Building Powerful RAGs for AI training takes place over 4 days. The program is structured in progressive modules for optimal skill development.

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 Training LlamaIndex - Building Powerful RAGs for AI training are: Python basics, elementary knowledge of AI and LLMs, installed development environment.. A preliminary interview validates your eligibility.

Is the training available in person?

Yes, the Training LlamaIndex - Building Powerful RAGs for AI training is available in remote. The format can be adapted to your needs (inter, intra, custom).

Training LlamaIndex - Building Powerful RAGs for AI | 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 Training LlamaIndex - Building Powerful RAGs for 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.

Teaching resources provided

To ensure the quality of the Training LlamaIndex - Building Powerful RAGs for AI 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 Training LlamaIndex - Building Powerful RAGs for AI 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 access terms and deadlines

Training objectives

Master the fundamentals of LlamaIndex in certified professional training.

Develop skills in data indexing for efficient RAG.

Design retrieval pipelines tailored to enterprise needs.

Implement LlamaIndex applications with open-source LLMs.

Optimize queries for precise and fast responses.

Integrate LlamaIndex into real professional projects.

Certify skills in prompt engineering and RAG.

Training program

Module 1Introduction to LlamaIndex: installation and basic concepts (Python, LLMs, data connectors)

Discover LlamaIndex through guided Python installation, explore key concepts of LLMs and RAG, set up your environment with data loaders for PDF and web, perform your first simple indexing exercises, test basic queries on provided datasets, obtain a functional index as the day's deliverable.

Module 2LlamaIndex Ingestion and Indexing: advanced data structuring (embeddings, vector stores)

Dive into ingesting various documents with LlamaIndex, generate embeddings via HuggingFace, set up vector stores like FAISS or Pinecone, practice chunking and metadata enrichment on real enterprise cases, optimize the index for scalability, produce a complete vector index with quality metrics as practical deliverable.

Module 3Querying and RAG with LlamaIndex: retrieval engines and routing (routers, hybrid retrievers)

Build performant LlamaIndex query engines for RAG, implement semantic and hybrid retrievers, test intelligent routing on multi-source datasets, integrate post-processors to refine results, apply to professional scenarios like enterprise Q&A, deploy an interactive RAG app as the day's deliverable.

Module 4Advanced LlamaIndex Applications: agents, evaluation, and deployment (chains, observability)

Develop LlamaIndex agents with tools and memory, evaluate performance via LlamaIndex Eval, integrate observability with LangSmith, deploy on Streamlit or FastAPI, simulate a complete enterprise use case, finalize a deployable RAG project with source code and report as certifying deliverable.