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 RAG Pipeline Training - Building Reliable Contextualized AIs training are: Mastery of Python, basics in machine learning, and experience using LLMs such as GPT or Llama. A preliminary interview validates your eligibility.

Is the training available in person?

Yes, the RAG Pipeline Training - Building Reliable Contextualized AIs training is available in remote. The format can be adapted to your needs (inter, intra, custom).

RAG Pipeline Training - Building Reliable Contex... | 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

9 spots remaining for the next session

FormatParticipantsDateDetails

Which format do you prefer?

Methods, approaches, and teaching resources

The RAG Pipeline Training - Building Reliable Contextualized 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.

Teaching resources provided

To ensure the quality of the RAG Pipeline Training - Building Reliable Contextualized AIs 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 RAG Pipeline Training - Building Reliable Contextualized AIs 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 RAG pipeline principles for professional, certifiable applications

Develop advanced retrieval systems with vector embeddings

Implement complete RAG pipelines integrating LLMs and databases

Optimize performance to reduce hallucinations in enterprise settings

Evaluate and test RAG pipelines on real professional cases

Deploy scalable and secure RAG solutions

Training program

Module 1RAG Pipeline Fundamentals: Setup and First Retrievals (LangChain, Embeddings)

Introduction to key RAG concepts to enhance LLMs, quick environment setup with LangChain and Hugging Face, creation of your first pipeline including indexing of company PDF documents and texts, experimentation with OpenAI or Sentence Transformers embeddings, practical exercises on real datasets for semantic retrieval, generation of contextualized responses with code review by the trainer.

Module 2Advanced Retrieval in RAG Pipelines: Vector Stores and Intelligent Chunking (Chroma, Pinecone)

Deep dive into vector databases for precise retrieval, setup of ChromaDB and Pinecone to store millions of vectors, mastery of strategic chunking for long company documents, hybrid BM25 + dense retrieval search, practical workshops to ingest large corpora such as annual reports or internal FAQs, query optimization for improved precision, production of real-time testable prototypes.

Module 3Augmented Generation in RAG Pipelines: LLM Integration and Prompt Engineering

Full assembly of the RAG pipeline with LLMs such as GPT-4 or Mistral, prompt engineering for seamless retrieval and generation fusion, handling reranking and merging of relevant chunks, real-world cases on enterprise chatbots for internal Q&A, collaborative exercises to implement multi-hop retrieval, live debugging of generated responses, creation of your customized RAG pipeline with fidelity metrics.

Module 4RAG Pipeline Optimization: Reducing Hallucinations and Scaling (Evaluation Metrics)

Advanced techniques to evaluate RAG using RAGAS and metrics like faithfulness or answer relevance, identification and correction of hallucinations via grounding, optimization of fine-tuned embeddings on company data, horizontal scaling with FAISS or Weaviate, workshops on A/B testing pipelines, 50% latency reduction via intelligent caching, production of actionable team evaluation reports.

Module 5RAG Pipeline Deployment: Production and Monitoring (Docker, Streamlit, Observability)

Containerization of the RAG pipeline with Docker and deployment on AWS or Vercel cloud, secure API integration for enterprise apps, real-time monitoring with LangSmith or Prometheus, cost management and auto-scaling, real-world use cases like HR virtual assistants or customer support, finalization of your ongoing project with live deployment, delivery of optimized source code and professional maintenance plan.