Rigorous trainer selection
Each trainer is validated on three criteria: hands-on field expertise, proven pedagogy and alignment with your industry.
- Triple validation: technical, pedagogical, sectoral.
- Minimum rating 4.8/5 over the last 12 sessions.
Learning objectives
- Master LLM-as-judge protocols for automatically evaluating AI outputs in professional training
- Develop skills in designing optimized judge prompts for certifying companies
- Implement scalable evaluation pipelines with LLMs as reliable arbiters
- Optimize the robustness of AI judges against biases in professional contexts
- Design custom metrics to measure model performance in business settings
- Deploy LLM-as-judge solutions in production to accelerate AI iterations
- Obtain a recognized certification validating advanced AI self-evaluation skills
The Learni story
Founded by passionate learning and innovation experts, Learni's mission is to make professional training accessible to everyone, anywhere in the world. Our team operates in major hubs — London, New York, Boston — and internationally, to support talents and organizations in upskilling.
Don't let this gap widen
Why this program matters
Without this upskilling, your team accumulates a technological gap that translates directly into productivity loss.
Organizations that don't train their talents on key topics see their competitiveness drop.
Every quarter without training is a gap widening with competitors who invest.
The cost of inaction quickly exceeds that of well-targeted training.
Program
Module 1LLM-as-judge Fundamentals: Theoretical Principles and Benchmarks (Hugging Face, OpenAI API)
Discover the theoretical foundations of LLM-as-judge through concrete automatic evaluation cases, explore standard benchmarks like MT-Bench and Arena-Hard, configure your first judge prompts with GPT-4 and Llama, analyze correlations with human evaluation on real datasets, complete practical exercises to identify initial pitfalls, produce a preliminary comparison report on AI judges, while integrating open-source tools for immediate business implementation.
Module 2Advanced LLM-as-judge Methods: Chain-of-Thought Prompts and Multi-Judges (LangChain, Custom Fine-Tuning)
Dive into sophisticated prompt engineering techniques for LLM-as-judge, implement step-by-step reasoning chains with LangChain, test multi-judge ensembles to reduce biases, fine-tune models like Mistral on custom evaluation datasets, evaluate inter-judge consistency on text and code generation tasks, develop automated Python scripts for scalable benchmarks, generate deliverables including performance dashboards to monitor AI outputs in real time.
Module 3Practical LLM-as-judge Implementation: End-to-End Pipelines (Docker, AWS SageMaker)
Build complete LLM-as-judge pipelines in containerized environments with Docker, deploy on AWS SageMaker for large-scale evaluations, integrate APIs like Vertex AI for hybrid judges, process massive datasets of 10k+ samples with GPU optimization, debug judgment errors via advanced logs, conduct hands-on workshops on real business cases like chatbot evaluation, produce a functional prototype ready for certifying production.
Module 4Optimization and Use Cases for LLM-as-judge: RLHF and Production (vLLM, Cost/Effectiveness Evaluation)
Optimize LLM-as-judge for RLHF using distillation and quantization techniques via vLLM, analyze cost/performance trade-offs on cloud deployments, apply to concrete cases like auditing generative models in business, measure impact on development cycles reduced by 40%, test robustness against adversarial attacks, finalize a capstone project with executive report and source code, validate skills via Qualiopi certification for immediate team integration.
Evaluation method
- Technical quizzes and MCQs on LLM-as-judge protocols (20%)
- Practical evaluation pipeline project (50%)
- Real case study with report and oral presentation (30%)
Learning method
- Active pedagogy with 70% hands-on practice
- Exercises on real datasets and professional tools
- Individualized feedback in small groups
- 3-month post-training support included
Methods, materials and delivery
The Training LLM-as-judge - Automating Precise AI Evaluation program is delivered onsite or remote (blended-learning, e-learning, virtual classroom, remote presence). At Learni, an industry-certified training organization, every program is built to maximize skills acquisition regardless of the chosen format.
The trainer alternates between demonstrative, interrogative and active methods (through hands-on labs and/or scenarios). This pedagogical approach guarantees concrete learning that's immediately applicable at work.
Equipment required
For the smooth delivery of the Training LLM-as-judge - Automating Precise AI Evaluation program, the following equipment is required:
- Mac or PC computers, high-speed fiber internet, whiteboard or flipchart, projector or interactive touch screen (for remote sessions)
- Training environments installed on workstations or accessible online
- Course materials, hands-on exercises and complementary resources
- Post-training access to materials and educational resources
For intra-company training on a site outside Learni, the client commits to providing all required teaching materials (computers, internet, etc.) for the smooth delivery of the program in line with the prerequisites in the communicated program.
* contact us for remote delivery feasibility** ratio varies depending on the program
Skills assessment methods
Assessment of skills acquired during the Training LLM-as-judge - Automating Precise AI Evaluation program is performed through:
- During training: case studies, hands-on labs and professional scenarios
- End of training: self-assessment questionnaire and skills evaluation by the trainer
- After training: completion certificate detailing acquired skills
Program accessibility
Learni is committed to making its programs accessible. All our programs are accessible to people with disabilities. Our teams are available to adapt the pedagogical methods to your specific needs. Please contact us for any adjustment request.
Enrollment terms and lead times
Learni programs are available inter-company and intra-company, onsite or remote. Enrollments are possible up to 48 business hours before the program starts. Our programs are eligible for corporate funding paths. Contact us to discuss your training project and funding options.
Verified reviews
What our learners
★★★★★
« cool, j'ai appris des trucs »
★★★★★
« j'etais perdu au debut mais Ramy Saharaoui m'a pas laché, il a pris le temps. merci vraiment »
★★★★★
« la formation dev etait intense mais grave bien. merci Anthony Khelil »
★★★★★
« 😊👍 »
★★★★★
« bien 👍 »
★★★★★
« Allan Busi t'es au top, continue comme ça. formation géniale »
★★★★★
« cool, j'ai appris des trucs »
★★★★★
« j'etais perdu au debut mais Ramy Saharaoui m'a pas laché, il a pris le temps. merci vraiment »
★★★★★
« la formation dev etait intense mais grave bien. merci Anthony Khelil »
★★★★★
« 😊👍 »
★★★★★
« bien 👍 »
★★★★★
« Allan Busi t'es au top, continue comme ça. formation géniale »
★★★★★
« cool, j'ai appris des trucs »
★★★★★
« j'etais perdu au debut mais Ramy Saharaoui m'a pas laché, il a pris le temps. merci vraiment »
★★★★★
« la formation dev etait intense mais grave bien. merci Anthony Khelil »
★★★★★
« 😊👍 »
★★★★★
« bien 👍 »
★★★★★
« Allan Busi t'es au top, continue comme ça. formation géniale »
Our method
Training quality, guaranteed at every step
Before, during, after: we frame the brief, introduce the trainer, tailor the content and measure impact. You stay in control from kickoff to wrap-up.
You meet the trainer beforehand
30-minute video call between you and the selected trainer to validate the fit, adjust content and clear any final doubts.
- Live briefing on goals and team context.
- Veto right — we swap the trainer for free if needed.
Content tailored to your context
No recycled slides. The syllabus is reworked from your real cases: tools, constraints, vocabulary, ongoing projects.
- Hands-on cases drawn from your stack and projects.
- Program co-written then validated by your team.
Continuous quality follow-up
Live evaluations, 30/90/180-day check-ins and a consolidation plan. If the impact misses the mark, we rework it.
- NPS, knowledge quizzes and skills self-assessment.
- Satisfaction guarantee: fully satisfied or free rework.
A simple promise: you don't pay to discover the trainer on day one. Everything is validated upfront, by you.
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