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 Corrective RAG - Correcting LLMs for Precise AI training are: Mastery of Python, basics in RAG, vector embeddings and LLM APIs. A preliminary interview validates your eligibility.

Is the training available in person?

Yes, the Training Corrective RAG - Correcting LLMs for Precise AI training is available in distanciel. The format can be adapted to your needs (inter, intra, custom).

Training Corrective RAG - Correcting LLMs for… | 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 Corrective RAG - Correcting LLMs for Precise 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 Corrective RAG - Correcting LLMs for Precise 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 Corrective RAG - Correcting LLMs for Precise 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 objectives

Master Corrective RAG techniques for reliable AI responses in business

Develop self-correcting pipelines integrating critique and reranking

Implement retrieval validation tools in professional projects

Optimize LLM accuracy with certified feedback loops

Design resilient RAG architectures for critical applications

Deploy scalable Corrective RAG systems in production

Training program

Module 1Fundamentals of Corrective RAG: Diagnosing Retrieval Failures (LangChain, FAISS)

Analysis of classic limitations of standard RAG through real business cases, hands-on with LangChain to implement automated diagnosis of erroneous retrievals, practical exercises on internal datasets with FAISS for vector stores, identification of LLM hallucinations, creation of an initial weaknesses report and personalized correction plan, while measuring immediate business impact.

Module 2Critique Mechanisms in Corrective RAG: Self-Evaluation and Reranking (LlamaIndex, HuggingFace)

Deployment of critique modules with LlamaIndex to evaluate the relevance of retrieved chunks, integration of HuggingFace models for real-time scoring and reranking, practical workshops on your company corpus, simulation of failure scenarios with feedback loops, production of a functional Corrective RAG prototype, optimization of accuracy metrics for certified AI responses.

Module 3Advanced Corrective RAG Pipelines: Multi-Retriever Fusion (Pinecone, SentenceTransformers)

Design of hybrid pipelines fusing multiple retrievers via Pinecone and SentenceTransformers, exercises on handling ambiguous queries in professional contexts, implementation of semantic correctors with intelligent rerouting, A/B testing on real business cases, generation of scalable deliverables like a monitoring dashboard, acceleration of skills for rapid and reliable deployments.

Module 4Feedback Integration in Corrective RAG: Learning Loops (OpenAI API, Custom Evaluators)

Implementation of human/AI feedback loops with OpenAI API to continuously refine retrievals, development of custom evaluators on your sensitive data, collaborative workshops to simulate user interactions in production, optimization of token costs via targeted corrections, realization of a complete self-improvement system, enhancement of certified skills to boost your internal AI projects.

Module 5Production Deployment of Corrective RAG: Monitoring and Scaling (Docker, Kubernetes Basics)

Containerized deployment with Docker for resilient Corrective RAG, configuration of advanced monitoring on Kubernetes basics and Prometheus, high-load simulations in business contexts, securing pipelines with anti-hallucination safeguards, finalization of the ongoing project with expert code review, delivery of a post-training maintenance roadmap and immediate team integration.