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 LangSmith 2026 - Mastering Professional LLM Observability training are: Python basics and introductory knowledge of LangChain or LLMs. A preliminary interview validates your eligibility.

Is the training available in person?

Yes, the Training LangSmith 2026 - Mastering Professional LLM Observability training is available in remote. The format can be adapted to your needs (inter, intra, custom).

Training LangSmith 2026 - Mastering Professional... | 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 LangSmith 2026 - Mastering Professional LLM Observability 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 LangSmith 2026 - Mastering Professional LLM Observability 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 LangSmith 2026 - Mastering Professional LLM Observability 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 the fundamentals of LangSmith 2026 for tracing professional LLM chains

Develop observability skills to optimize enterprise AI apps

Configure datasets and certified automated evaluations with LangSmith

Implement real-time debugging on concrete LLM projects

Design scalable monitoring pipelines for Qualiopi-certified teams

Deploy collaborative traces and analyze LLM performances

Training program

Module 1LangSmith 2026 Fundamentals: Installation and User Interface (Basic Tracing)

Immersive discovery of LangSmith 2026 through quick cloud and local environment installation, intuitive dashboard interface exploration to visualize initial LLM traces, collaborative project setup with secure API keys, practical exercises on simple LangChain chain logging, creation of your first traceable run and initial report exports, building immediate confidence on real enterprise cases.

Module 2Advanced LangSmith 2026 Tracing: Datasets and Evaluations (Automated Tools)

Hands-on creation of custom datasets to test LLMs, use of built-in evaluators like LLM-as-judge for automatic response scoring, A/B testing setup on varied prompts, collaborative manual annotation exercises and precise metrics generation, direct application to a red thread conversational AI app project, leveraging insights for faster professional team iteration.

Module 3LangSmith 2026 Debugging and Monitoring: Real-Time Analysis (Alerts, Spans)

Master interactive debugging with detailed spans and full-text search on massive logs, custom alerts configuration for detecting critical LLM latencies or errors, failing scenario simulations on complex multi-agent chains, root cause analysis exercises with visual graphs and timelines, collaborative prompt optimization via feedback loops, actionable reports for secure, scalable enterprise deployment.

Module 4Professional LangSmith 2026 Deployment: Pipelines and Integrations (CI/CD, Collaboration)

Advanced LangSmith integration into CI/CD pipelines with GitHub Actions and Docker, continuous monitoring setup for production LLM apps, team trace sharing and certified dataset versioning exercises, cost/performance optimization cases on AWS or GCP clouds, red thread project finalization with custom dashboard and maintenance plan, skills ready to boost professional AI projects immediately.