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Training Corrective RAG - Optimize AI Pipelines in Production

Ref: IBL188
10 people max.
5500€ HT / per person
−15% from 2 people−30% from 3 people−50% from 5 people
Pay in 3 installments · +$170/day onsite · +$500 with certification exam
5 journées
distanciel

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Learning objectives

  • Master corrective RAG techniques to optimize LLM response accuracy in certified professional training.
  • Develop expert skills in diagnosing and correcting hallucinations in enterprise RAG systems.
  • Design advanced RAG pipelines integrating React and Angular for reactive web interfaces.
  • Implement reranking and fine-tuning strategies in a Qualiopi-certified professional context.
  • Optimize RAG performance with tools like LangChain and LlamaIndex for scalable projects.
  • Deploy robust corrective RAG solutions, evaluable in enterprise production.

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.

Allan Busi
Allan Busi

Learni Trainer · Expert

73%productivity gap
×3cost of inaction

Program

Module 1Fundamentals of Corrective RAG: Diagnosing Hallucinations (React, LangChain)

Dive into corrective RAG mechanisms through practical workshops on React to visualize data flows, analyze common LLM errors with LangChain tools, correct biases in real-time on real datasets, implement initial rerankers based on cross-encoders, produce a deliverable diagnostic report, while integrating HTML/CSS for interactive dashboards, building professional skills in 7 intensive hours.

Module 2Advanced Corrective RAG Pipelines: Hybrid Retrieval (Angular, FAISS)

Build hybrid corrective RAG pipelines in Angular for dynamic interfaces, integrate FAISS and Pinecone for multi-modal retrieval, test automated corrections on concrete business cases, optimize embeddings with HuggingFace fine-tuning, generate adaptive queries via LLM chaining, deploy scalable web prototypes, evaluate accuracy with RAGAS metrics, accumulating 7 hours of certifiable hands-on exercises.

Module 3Corrective RAG Optimization: Reranking and Fusion (React, LlamaIndex)

Master corrective RAG reranking in React with LlamaIndex for intelligent chunk fusion, simulate degraded production scenarios, apply LLM self-correction techniques, integrate human-AI feedback loops, measure quantified performance gains, create reusable Angular modules for monitoring, produce enterprise-adapted open-source deliverables, through 7 hours of collaborative challenges in-person.

Module 4Corrective RAG Security and Scalability: Guardrails (HTML/CSS, LangGraph)

Strengthen corrective RAG security with guardrails in HTML/CSS for secure UIs, deploy LangGraph for resilient workflows, manage prompt injection attacks in production, scale on Kubernetes with Angular state management, audit real vulnerabilities, implement Drift monitoring via EvidentlyAI, generate GDPR compliance reports, in 7 hours of intensive labs fostering expert skill development.

Module 5Production Deployment of Corrective RAG: Fullstack Monitoring (React/Angular)

Finalize corrective RAG deployment in React-Angular stack for live web apps, configure observability with Prometheus and Grafana, automate corrections via CI/CD GitHub Actions, test A/B on real users, optimize AWS/GCP cloud costs, produce certifiable project portfolio, review famous failure cases, conclude with enterprise Q&A in 7 hours, validating professional expertise.

Evaluation method

  • Advanced technical quiz on corrective RAG and RAGAS metrics.
  • Final project: optimized RAG pipeline deployed in React/Angular.
  • Qualiopi certificate with deliverables portfolio and 360° feedback.

Learning method

  • 70% hands-on: coding workshops on React, Angular, LangChain.
  • Real business cases: quantified diagnostics of failing RAG systems.
  • Agile methods: pairing, group code reviews max 10 participants.
  • Pro tools: Jupyter, VS Code, Docker for expert pipelines.

Methods, materials and delivery

The Training Corrective RAG - Optimize AI Pipelines in Production 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 Corrective RAG - Optimize AI Pipelines in Production 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 Corrective RAG - Optimize AI Pipelines in Production 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

4.9 · +100 verified reviews
★★★★★

« cool, j'ai appris des trucs »

TomFormation AWS — Cloud Practitioner
★★★★★

« j'etais perdu au debut mais Ramy Saharaoui m'a pas laché, il a pris le temps. merci vraiment »

Eva CarpentierFormation LLM en Entreprise — Claude, ChatGPT, Mistral
★★★★★

« la formation dev etait intense mais grave bien. merci Anthony Khelil »

NolanDWWM - Développeur Web et Web Mobile
★★★★★

« 😊👍 »

AmbreDWWM - Développement Web & Mobile React
★★★★★

« bien 👍 »

Léo BlanchardFormation AWS — DevOps Engineer Professional
★★★★★

« Allan Busi t'es au top, continue comme ça. formation géniale »

MargotFormation Claude & ChatGPT — Comparatif et Cas d'Usage
★★★★★

« cool, j'ai appris des trucs »

TomFormation AWS — Cloud Practitioner
★★★★★

« j'etais perdu au debut mais Ramy Saharaoui m'a pas laché, il a pris le temps. merci vraiment »

Eva CarpentierFormation LLM en Entreprise — Claude, ChatGPT, Mistral
★★★★★

« la formation dev etait intense mais grave bien. merci Anthony Khelil »

NolanDWWM - Développeur Web et Web Mobile
★★★★★

« 😊👍 »

AmbreDWWM - Développement Web & Mobile React
★★★★★

« bien 👍 »

Léo BlanchardFormation AWS — DevOps Engineer Professional
★★★★★

« Allan Busi t'es au top, continue comme ça. formation géniale »

MargotFormation Claude & ChatGPT — Comparatif et Cas d'Usage
★★★★★

« cool, j'ai appris des trucs »

TomFormation AWS — Cloud Practitioner
★★★★★

« j'etais perdu au debut mais Ramy Saharaoui m'a pas laché, il a pris le temps. merci vraiment »

Eva CarpentierFormation LLM en Entreprise — Claude, ChatGPT, Mistral
★★★★★

« la formation dev etait intense mais grave bien. merci Anthony Khelil »

NolanDWWM - Développeur Web et Web Mobile
★★★★★

« 😊👍 »

AmbreDWWM - Développement Web & Mobile React
★★★★★

« bien 👍 »

Léo BlanchardFormation AWS — DevOps Engineer Professional
★★★★★

« Allan Busi t'es au top, continue comme ça. formation géniale »

MargotFormation Claude & ChatGPT — Comparatif et Cas d'Usage
Read all reviews
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.

Step 1

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.
Step 2

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.
Step 3

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.
Step 4

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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