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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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The Training Guardrails.ai - Securing LLM Outputs in Production 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.
To ensure the quality of the Training Guardrails.ai - Securing LLM Outputs in Production training, Learni provides the following 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.
The assessment of skills acquired during the Training Guardrails.ai - Securing LLM Outputs in Production training is carried out through:
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Learni training programs are available for inter-company and intra-company settings, both in-person and remote. Registration is possible up to 48 business hours before the start of training. Our programs are eligible for OPCO, Pôle emploi, and FNE-Formation funding. Contact us to discuss your training project and funding possibilities.
Installation and configuration of Guardrails.ai. Understanding RAIL specifiers. Creating ready-to-use validators. Hands-on practice on erroneous LLM outputs. Professional exercises: correcting hallucinations. Basic integration with OpenAI. Red thread project: simple validation pipeline. Real enterprise cases in generative AI. Unit tests on validators. Error handling and automated retries. Basic logging demo. Collaborative implementation in remote format.
Advanced custom validators with Pydantic. Integration with LangChain and LlamaIndex. Production deployment with Docker. Monitoring and performance metrics. Optimization for high-volume LLMs. Exercises: multi-LLM guardrails. Red thread project finalized and deployed. Enterprise use cases: secure chatbots. AI DevOps best practices. API cost management. Real-time evaluation. Post-training support initiated.
Target audience
AI Developers, Data Scientists, and ML Engineers building skills in LLM security
Prerequisites
Mastery of Python, experience using LLMs such as OpenAI or Hugging Face
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