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 Self-RAG 2026 - Creating Autonomous AI Agents training are: Python basics, knowledge of LLMs and vector embeddings. A preliminary interview validates your eligibility.

Is the training available in person?

Yes, the Training Self-RAG 2026 - Creating Autonomous AI Agents training is available in remote. The format can be adapted to your needs (inter, intra, custom).

Training Self-RAG 2026 - Creating Autonomous 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 Self-RAG 2026 - Creating Autonomous AI Agents 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 Self-RAG 2026 - Creating Autonomous AI Agents 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 Self-RAG 2026 - Creating Autonomous AI Agents 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 Self-RAG 2026 for professional AI agents

Develop intelligent and adaptive retrieval systems

Implement augmented generation with auto-correction in business environments

Design certifying Self-RAG pipelines for real-world applications

Optimize the performance of autonomous AI agents in production

Integrate Self-RAG 2026 into professional data workflows

Training program

Module 1Self-RAG 2026 Fundamentals: Installation and Architecture (Python, LangChain, embeddings)

Immersive discovery of Self-RAG 2026 principles, quick installation of the environment with Python and key libraries like LangChain and Hugging Face, exploration of vector embeddings for relevant retrieval, first practical exercises on company datasets, creation of a basic agent that independently decides its information needs, validation through unit tests to consolidate skills from the first day.

Module 2Adaptive Retrieval in Self-RAG 2026: Indexing and Search (FAISS, Pinecone, vector stores)

Building performant vector indexes with FAISS and Pinecone, implementation of critical and adaptive retrieval mechanisms specific to Self-RAG 2026, exercises on concrete cases like enterprise Q&A, query optimization to minimize hallucinations, integration of feedback loops to refine results in real-time, development of a functional retrieval prototype with live precision metrics evaluation.

Module 3Generation and Auto-Correction in Self-RAG 2026: Advanced LLMs (Llama, GPT, reflection tokens)

Integration of LLMs like Llama or GPT into Self-RAG 2026 pipelines, development of reflection tokens for intelligent self-evaluation, practical exercises on augmented generation with dynamic correction, simulation of complex business scenarios like financial report analysis, creation of iterative critiques to enhance reliability, delivery of a complete agent capable of self-improvement on real tasks.

Module 4Deployment and Optimization of Self-RAG 2026: Scaling and Monitoring (Docker, Streamlit, observability)

Production deployment of Self-RAG 2026 agents via Docker and Streamlit for intuitive user interfaces, optimization of costs and performance with advanced monitoring, case studies on scalable enterprise applications, debugging and tuning exercises for maximum robustness, finalization of the red thread project with certifying evaluation, personalized action plan to integrate these skills into the company immediately.