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 LangSmith Training - Optimizing the Debugging of LLM Applications training are: Advanced proficiency in Python, LangChain, and production LLM application development. A preliminary interview validates your eligibility.

Is the training available in person?

Yes, the LangSmith Training - Optimizing the Debugging of LLM Applications training is available in distanciel. The format can be adapted to your needs (inter, intra, custom).

LangSmith Training - Optimizing the Debugging of… | 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 LangSmith Training - Optimizing the Debugging of LLM Applications 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 LangSmith Training - Optimizing the Debugging of LLM Applications 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 LangSmith Training - Optimizing the Debugging of LLM Applications 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 advanced LangSmith traces for debugging complex LLM chains

Develop custom enterprise evaluators to validate performance

Implement real-time monitoring of certifying AI workflows

Design datasets and automated tests with professional LangSmith

Optimize LLM agents for enhanced scalability and reliability

Deploy secure and traceable production pipelines

Acquire certifying skills in AI observability

Training program

Module 1Advanced LangSmith Traces: Expert Configuration and Instrumentation (LangChain, logging tools)

Immersion in fine-grained LangSmith instrumentation to capture detailed traces of complex LLM chains, configuration of development environments with advanced LangChain integration, practical exercises on instrumenting multi-step agents, real-time log analysis with custom dashboards, creation of your first professional runset to identify bottlenecks, immediate trainer feedback on initial optimization.

Module 2Custom LangSmith Evaluators: Design and Expert Metrics (custom metrics, benchmarks)

Development of custom evaluators with LangSmith to assess LLM response fidelity and coherence, use of Golden Datasets for rigorous benchmarks, implementation of composite metrics like hallucination rate and latency, real enterprise cases on RAG systems, collaborative iterations with automated feedback, production of exportable evaluation reports ready for internal audits, immediate gain in precision of your AI models.

Module 3LangSmith Datasets and Testing: Expert Automation (runs, collaborative annotations)

Construction of annotated datasets via LangSmith interface for exhaustive LLM application testing, automation of parallel runs on thousands of examples, team collaboration for expert annotations, integration with CI/CD for recurring tests, exercises on interactive debugging of recurrent failures, generation of custom playgrounds for rapid prototyping, concrete valorization of your datasets as reusable enterprise assets.

Module 4LangSmith Monitoring and Agents: Expert Scalability (alerts, production workflows)

Implementation of proactive monitoring with LangSmith alerts on LLM performance degradations, optimization of autonomous agents via granular traces, scaling of distributed workflows with distributed tracing, advanced use cases on multi-agents and tool calling, configuration of guards and human-in-the-loop, production simulation exercises for high availability, assurance of total observability for your critical deployments.

Module 5LangSmith Production and Optimization: Certifying Deployment (enterprise integrations, security)

Secure production deployment of LangSmith pipelines with SSO and RBAC, expert integrations with observability stacks like Datadog or Prometheus, final optimization of costs and performance via LangSmith insights, review of ongoing project with certification of acquired skills, post-training action plan for immediate ROI in the enterprise, export of reusable templates, closure with MCQ and presentation for professional certifying validation.