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.

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 Semantic Chunking - Optimising Advanced RAGs in 2026 training are: Mastery of Python, vector embeddings, and basics of LLMs such as GPT or Llama. A preliminary interview validates your eligibility.

Is the training available in person?

Yes, the Training Semantic Chunking - Optimising Advanced RAGs in 2026 training is available in remote. The format can be adapted to your needs (inter, intra, custom).

Training Semantic Chunking - Optimising Advanced... | Learni

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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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Methods, approaches, and teaching resources

The Training Semantic Chunking - Optimising Advanced RAGs in 2026 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 Semantic Chunking - Optimising Advanced RAGs in 2026 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 Semantic Chunking - Optimising Advanced RAGs in 2026 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 objectives

Master semantic chunking algorithms for precise retrievals in enterprise settings

Develop skills in adaptive semantic chunking for professional RAG

Design optimized chunking pipelines integrating embeddings and 2026 LLMs

Implement hybrid chunking strategies to reduce hallucinations

Optimize vector store performance with intelligent certified chunking

Deploy scalable RAG applications based on advanced semantic chunking

Training program

Module 1Fundamentals of Semantic Chunking: embeddings and semantic boundary detection (SentenceTransformers, spaCy)

Dive into the principles of semantic chunking by installing a dedicated Python environment with Hugging Face and SentenceTransformers, calculate embeddings on various corpora to identify natural semantic breaks, practice exercises on real enterprise texts like financial or legal reports, generate your first coherent chunks and visualize vector similarities, produce a comparative analysis report with fixed chunking to measure retrieval precision gains.

Module 2Advanced Semantic Chunking Algorithms: hierarchical and adaptive chunking (LangChain, LlamaIndex)

Explore hierarchical semantic chunking algorithms by integrating LangChain and LlamaIndex for multi-level splitting, test methods like cosine similarity chunking and knowledge graphs, apply these techniques to large enterprise datasets with real customer support cases, dynamically adjust chunk sizes based on semantic density, develop a functional RAG prototype and evaluate its performance using metrics like recall@K, deliver an optimized script ready for production.

Module 3Integrating Semantic Chunking into RAG: hybridisation and optimisation (FAISS, Pinecone)

Integrate semantic chunking into complete RAG pipelines by connecting your chunks to vector stores like FAISS or Pinecone, hybridize with keyword search for robust retrieval, simulate complex enterprise queries on internal knowledge bases, optimize post-chunking rerankings with cross-encoder models, handle challenges like multimodal documents, build an end-to-end RAG system reducing hallucinations by 40%, and document your technical choices for immediate implementation.

Module 4Deployment and 2026 Semantic Chunking Trends: scalability and monitoring (Docker, Prometheus)

Prepare the deployment of your semantic chunking solutions by containerizing with Docker and Kubernetes for enterprise scale, integrate RAG performance monitoring via Prometheus and Grafana, anticipate 2026 evolutions like multimodal chunking and LLM agents, test in real conditions on a personalized red thread project, optimize cloud costs by refining embeddings, finalize with a collective code review and maintenance plan, leave with a concrete portfolio certifying your professional skills.