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Training RAG Pipeline - Deploying Precise AI Systems

Ref: KQL248
10 people max.
7000€ 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 RAG pipeline architecture for professional certifying applications
  • Develop advanced retrievers and high-performance vector indexes
  • Implement retrieval-augmented generation with prompt optimization
  • Design scalable RAG pipelines for enterprises
  • Optimize RAG performance with evaluation and fine-tuning
  • Deploy RAG solutions in production with monitoring
  • Acquire certifying RAG skills to advance AI expertise

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.

Fouzi Benzidane
Fouzi Benzidane

Learni Trainer · Expert

73%productivity gap
×3cost of inaction

Program

Module 1Architecture: Designing Advanced RAG Pipelines (LangChain, LlamaIndex, embeddings)

In-depth analysis of RAG pipeline components, installation of expert environments with LangChain and LlamaIndex, creation of custom embeddings via HuggingFace and OpenAI, configuration of vector stores like FAISS and Pinecone, practical exercises on enterprise datasets to index large document volumes, development of a functional RAG prototype with initial precision tests, trainer feedback to refine the architecture from day one.

Module 2Retrieval: Optimizing Semantic Search in RAG Pipelines (hybrids, reranking)

Exploration of dense and sparse retrievers for RAG pipelines, implementation of hybrid search combining BM25 and embeddings, integration of rerankers like Cohere Rerank to boost relevance, strategic chunking on complex documents, exercises on real enterprise cases with multilingual queries, comparative evaluation of recall/precision metrics, production of a deployable retrieval module with detailed logging for analysis.

Module 3Augmentation: Integrating LLM Generation in RAG Pipelines (prompt engineering, agents)

Design of dynamic prompts for RAG pipelines, chaining retriever and generator with LLMs like Llama3 or GPT-4, development of autonomous RAG agents via LangGraph, management of long contexts and compression, practical workshops on sensitive enterprise Q&A scenarios, A/B tests to minimize hallucinations, creation of a complete retrieval-to-generation flow with ethical guardrails, documentation of reusable patterns for teams.

Module 4Optimization: Scaling and Evaluating RAG Pipelines (fine-tuning, advanced metrics)

Fine-tuning of domain-specific embeddings and retrievers via LoRA on enterprise datasets, implementation of automated evaluation with RAGAS and TruLens, latency optimization via caching and batching, exercises on horizontal scaling with Docker and Kubernetes, analysis of API costs and open-source alternatives, development of a monitoring dashboard for RAG pipelines, iterations on the ongoing project to achieve 95% production precision.

Module 5Deployment: Producing RAG Pipelines for Enterprise (CI/CD, monitoring, security)

Full-stack deployment of RAG pipelines on AWS/GCP cloud with FastAPI and Streamlit, CI/CD setup via GitHub Actions and Docker, monitoring integration with Prometheus/Grafana for drift detection, securing sensitive data and API keys, live incident simulations for resilience, finalization of the certifying ongoing project with presentation, delivery of optimized source code and maintenance plan for immediate enterprise deployment.

Evaluation method

  • Expert multiple-choice quiz to validate acquired knowledge at the end of the training
  • Continuous assessment through practical RAG exercises
  • Presentation of the ongoing RAG pipeline project to the trainer

Learning method

  • Courses led by an active AI expert trainer
  • Hands-on exercises on real enterprise RAG cases
  • Progressive ongoing project throughout the training
  • Complete course materials provided to each participant

Methods, materials and delivery

The Training RAG Pipeline - Deploying Precise AI Systems 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 RAG Pipeline - Deploying Precise AI Systems 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 RAG Pipeline - Deploying Precise AI Systems 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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