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 - Optimize RAG and AI Retrieval training are: Mastery of Python, basics in NLP, embeddings, and vector stores such as FAISS. A preliminary interview validates your eligibility.

Is the training available in person?

Yes, the Training Semantic Chunking - Optimize RAG and AI Retrieval training is available in distanciel. The format can be adapted to your needs (inter, intra, custom).

Training Semantic Chunking - Optimize RAG and 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.

Configure your training

Response within 24hNo commitment100% free

7 spots remaining for the next session

FormatParticipantsDateDetails

Which format do you prefer?

Methods, approaches, and teaching resources

The Training Semantic Chunking - Optimize RAG and AI Retrieval 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 - Optimize RAG and AI Retrieval 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 - Optimize RAG and AI Retrieval 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 professional RAG pipelines

Develop skills in adaptive semantic chunking in enterprise settings

Implement embedding tools for intelligent, certified chunking

Optimize retrieval accuracy in real-world AI applications

Design scalable and performant text preprocessing workflows

Evaluate and refine chunking strategies to minimize LLM hallucinations

Deploy certified semantic chunking solutions in production

Training program

Module 1Fundamentals of Semantic Chunking: Principles and Vector Embeddings (SentenceTransformers, Cosine Similarity)

Dive into the basics of semantic chunking by comparing fixed-size and semantic approaches, install SentenceTransformers and Hugging Face to generate embeddings, practice cosine similarity calculations on real text datasets, analyze manual semantic breaks, build your first simple chunker with guided exercises, produce cluster visualizations to validate semantic coherence and anticipate RAG precision gains.

Module 2Semantic Chunking Algorithms: Adaptive Thresholds and Hierarchization (LangChain, Recursive Splitting)

Explore advanced algorithms such as similarity threshold chunking and recursive splitting, integrate LangChain to automate the process, test on large enterprise corpora, adjust parameters to balance granularity and context, perform comparative benchmarks with metrics like ROUGE, generate optimized chunks for vector stores, apply to a concrete case of legal documents, and export reusable pipelines.

Module 3Implementing Semantic Chunking: Python Tools and Vector Stores (FAISS, Pinecone)

Get hands-on with practical Python implementations, configure FAISS and Pinecone to index semantic chunks, develop a complete chunking-embedding-indexing pipeline, process massive datasets like Wikipedia dumps, optimize memory and speed via batching, test hybrid retrieval on complex queries, produce a functional RAG prototype with Streamlit, analyze performance, and iterate based on trainer feedback.

Module 4Optimizing Semantic Chunking: Hybridization and Evaluation (Precision and Recall Metrics)

Hone your skills by hybridizing semantic chunking with business rules and metadata, evaluate using advanced metrics like precision@K and NDCG, integrate feedback loops for self-improvement, apply to enterprise AI customer support cases, reduce LLM hallucinations by 40% live, benchmark against classic baselines, document optimization strategies, and prepare cloud scalability with AWS S3 and Lambda for production volumes.

Module 5Deploying Semantic Chunking: Production and Monitoring (Docker, MLflow)

Finalize with Docker container deployment, orchestrate via MLflow for experiment tracking, monitor semantic drift in real-time, integrate with LLM chains like LlamaIndex, test end-to-end on the enterprise red thread project, simulate high loads, generate ROI reports with time and precision gains, deliver certified source code, and plan maintenance and evolutions for robust production RAG.