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 - Optimizing LLM Tracing in Production training are: Mastery of Python, LangChain and Large Language Models (LLM). A preliminary interview validates your eligibility.

Is the training available in person?

Yes, the Training LangSmith - Optimizing LLM Tracing in Production training is available in remote. The format can be adapted to your needs (inter, intra, custom).

Training LangSmith - Optimizing LLM Tracing in P... | 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 - Optimizing LLM Tracing 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 LangSmith - Optimizing LLM Tracing 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 LangSmith - Optimizing LLM Tracing 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 LangSmith for tracing LLM chains in a professional context

Develop advanced skills in certified AI application debugging

Implement complete observability of LLM workflows in the enterprise

Design high-performance evaluation datasets with LangSmith

Optimize AI agent performance via advanced monitoring

Deploy scalable and secure LLM pipelines in production

Integrate LangSmith into enterprise projects for efficiency gains

Training program

Module 1Advanced LangSmith Fundamentals and Professional Configuration

Installation and setup of LangSmith for enterprise projects. Configuration of API keys and collaborative projects. Exploration of advanced LLM traces. Hands-on practice with concrete debugging cases. Creation of the red thread project: complex LLM chain. Exercises on LangChain-LangSmith integration. Management of secure test environments. Analysis of initial performance metrics. Implementation of custom logging. (112 mots)

Module 2Advanced Tracing and Observability with LangSmith

Implementation of distributed tracing for multi-step agents. Capture of LLM inputs/outputs in real time. Use of annotations for complex workflows. Practical exercises on business cases: optimized RAG. Analysis of latencies and token costs. Configuration of hubs for shared datasets. Red thread project: tracing a conversational agent. Hands-on with nested spans. Integration with external observability tools. Evaluation of recurring error patterns. (118 mots)

Module 3LangSmith Debugging and Performance Evaluation

Advanced interactive debugging techniques in LangSmith. Creation and management of custom evaluation datasets. Running benchmarks on various LLM models. Hands-on professional exercises: prompt tuning. Analysis of scores and advanced visualizations. Optimization of chains via feedback loops. Red thread project: debugging a failing RAG pipeline. A/B testing on LLM configurations. Management of biases and hallucinations. Export of reports for enterprise audits. (109 mots)

Module 4LangSmith Production Deployment and Monitoring

Scalable deployment strategies with LangSmith in production. Real-time monitoring and alerts configuration. CI/CD integration for LLM pipelines. Security of traces and GDPR compliance. Exercises on concrete cases: monitoring enterprise AI apps. Completion of the red thread project with custom dashboard. Cost optimization via LangSmith insights. Best practices for AI DevOps teams. Project presentation and code review. Post-training maintenance resources. (104 mots)