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 Guardrails.ai - Securing LLM Outputs in Production training are: Advanced mastery of Python, experience with LLMs (OpenAI, HuggingFace), knowledge of Pydantic and REST APIs. A preliminary interview validates your eligibility.

Is the training available in person?

Yes, the Training Guardrails.ai - Securing LLM Outputs in Production training is available in distanciel. The format can be adapted to your needs (inter, intra, custom).

Training Guardrails.ai - Securing LLM Outputs in... | Learni

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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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Methods, approaches, and teaching resources

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.

Teaching resources provided

To ensure the quality of the Training Guardrails.ai - Securing LLM Outputs in Production 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 Guardrails.ai - Securing LLM Outputs in Production 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 Guardrails.ai to validate LLM outputs in professional contexts

Develop custom validators tailored to business needs

Implement Guardrails.ai pipelines integrated into certifying CI/CD workflows

Optimize the reliability of AI applications with advanced guardrails

Set up real-time monitoring to detect LLM anomalies

Deploy scalable Guardrails.ai solutions in enterprise production

Training program

Module 1Guardrails.ai Fundamentals: Installation and First Validators (Pydantic, LLM Schemas)

Quick installation of the Guardrails.ai environment with pip and Docker, creation of your first professional project integrating basic validators on real LLM prompts, exploration of Pydantic schemas to structure expected outputs, practical exercises on enterprise cases such as secure financial report generation, automated unit tests, and first live validation with an OpenAI model, delivery of a functional deliverable ready for iteration.

Module 2Advanced Guardrails.ai Validators: Regex, LLMs, and Custom Functions (Output Reliability)

Deep dive into regex validators to filter sensitive content, use of LLM validators for intelligent contextual validation, development of custom functions tailored to your business data such as bias or hallucination detection, practical workshops on real enterprise datasets, chaining validators into complex pipelines, interactive debugging with detailed logs, production of actionable error reports, and performance optimization for real-time responses.

Module 3Guardrails.ai Integration: LangChain, FastAPI, and Enterprise APIs (Hybrid Workflows)

Seamless integration of Guardrails.ai into LangChain for robust LLM chains, development of secure REST APIs with FastAPI and Guardrails middleware, connection to PostgreSQL databases to persist validations, real enterprise cases such as a secure customer chatbot, refactoring exercises for existing applications, end-to-end integration tests with Postman, creation of a deployable prototype connected to your internal services, focus on horizontal scalability.

Module 4Guardrails.ai Performance: Monitoring, Scaling, and Optimization (Real-Time Enterprise)

Setup of advanced monitoring with Prometheus and Grafana to track Guardrails.ai validation rates, optimization of validators to reduce latency under 100ms, caching and batching techniques for massive LLM output volumes, workshops on scaling with Kubernetes, analysis of real bottlenecks via profiling, implementation of Slack alerts for critical anomalies, exercises on large enterprise datasets, production of a custom dashboard to monitor AI reliability KPIs.

Module 5Guardrails.ai Deployment: CI/CD, Production, and Certifying Best Practices (Security)

Setup of CI/CD pipelines with GitHub Actions and Docker to deploy Guardrails.ai to production, securing with secrets management and automated audits, best practices for GDPR compliance and Qualiopi certifications, simulation of production incidents with rapid rollback, pair-programming code review on your red thread project, finalization of the complete deliverable with API documentation and operational playbook, preparation for team autonomy and immediate enterprise adoption.