{"id":238511,"date":"2026-04-08T15:05:00","date_gmt":"2026-04-08T13:05:00","guid":{"rendered":"https:\/\/www.itta.net\/?post_type=formations&#038;p=238511"},"modified":"2026-04-12T18:59:05","modified_gmt":"2026-04-12T16:59:05","slug":"developing-llm-rag-applications-langchain","status":"publish","type":"formations","link":"https:\/\/www.itta.net\/en\/trainings\/artificial-intelligence\/ai-development-and-llm\/development-with-llms-and-agents\/developing-llm-and-rag-applications-with-langchain\/","title":{"rendered":"Developing LLM and RAG Applications with LangChain"},"content":{"rendered":"\r\n<p class=\"wp-block-paragraph\">The Developing LLM and RAG Applications with LangChain training course helps participants take the next step in designing generative AI applications. It teaches how to structure processing chains, integrate language models, orchestrate logical steps and build more robust and better-organized RAG applications.<\/p>\r\n\r\n\r\n\r\n<p><strong>An advanced training course to move from simple prototypes to orchestration logic<\/strong><\/p>\r\n\r\n\r\n\r\n<p class=\"wp-block-paragraph\">During this training course, participants discover how LangChain can be used to connect multiple application building blocks, manage context, chain processes, integrate document sources and build more advanced LLM-oriented applications.<\/p>\r\n","protected":false},"featured_media":0,"parent":0,"menu_order":0,"template":"","editeurs":[5025],"domaine":[4251,2982,4271,4244,2980],"class_list":["post-238511","formations","type-formations","status-publish","hentry","editeurs-langchain","domaine-ai-development-and-llm","domaine-artificial-intelligence","domaine-development-with-llms-and-agents","domaine-developpement-ia-et-llm","domaine-intelligence-artificielle"],"acf":{"reference":"AI-06-06","duree_nombre":2,"duree_unite":"jour","prix_virtuel":1400,"prix_presentiel":1500,"prix_blended":"","prix_elearning":"","views":82,"niveau":["Avanc\u00e9"],"role":[],"certifiant":"","pdus":"","lieux":["Gen\u00e8ve","Lausanne"],"formats_dapprentissage":["presentiel","virtuel"],"examen_inclus":"Non certifiant","garanti":[],"financement":["Temptraining"],"description_temptraining":"","description_caf":"","description_title":"Developing LLM and RAG Applications with LangChain Training","objectifs":"<ul>\r\n \t<li>Understand the role of LangChain in the LLM ecosystem<\/li>\r\n \t<li>Build processing chains around a model<\/li>\r\n \t<li>Orchestrate queries, context and outputs<\/li>\r\n \t<li>Structure a RAG application with document retrieval<\/li>\r\n \t<li>Prepare more modular and scalable LLM applications<\/li>\r\n \t<li>Identify best practices for design with LangChain<\/li>\r\n<\/ul>","connaissances":"<ul>\r\n \t<li>Solid foundations in software development<\/li>\r\n \t<li>Familiarity with LLM API principles<\/li>\r\n \t<li>A basic understanding of RAG is recommended<\/li>\r\n<\/ul>","profils_participants":"<ul>\r\n \t<li>Developers<\/li>\r\n \t<li>Technical architects<\/li>\r\n \t<li>Generative AI professionals<\/li>\r\n \t<li>Developers already familiar with LLM APIs<\/li>\r\n \t<li>Anyone looking to structure RAG and LLM applications with LangChain<\/li>\r\n<\/ul>","examen":"","documentation":"<ul>\n \t<li>Support de cours num\u00e9rique inclus<\/li>\n<\/ul>","lab__exercices":"<ul>\n \t<li>This course includes workshops on structuring chains, integrating context, setting up RAG components, organizing applications and building advanced use cases with LangChain.<\/li>\n<\/ul>","contenu_cours":"<strong>Module 1: Discovering LangChain and Its Positioning<\/strong>\r\n<ul>\r\n \t<li>Why use an orchestration framework<\/li>\r\n \t<li>Difference between a simple API call and chain logic<\/li>\r\n \t<li>Overview of LangChain components<\/li>\r\n \t<li>Identifying use cases where LangChain provides real value<\/li>\r\n<\/ul>\r\n<strong>Module 2: Building Processing Chains<\/strong>\r\n<ul>\r\n \t<li>Assembling multiple logical steps<\/li>\r\n \t<li>Structuring inputs and outputs<\/li>\r\n \t<li>Managing context and intermediate transformations<\/li>\r\n \t<li>Preparing more robust processes<\/li>\r\n<\/ul>\r\n<strong>Module 3: Managing Context and Document Sources<\/strong>\r\n<ul>\r\n \t<li>Understanding RAG logic with LangChain<\/li>\r\n \t<li>Preparing a document base<\/li>\r\n \t<li>Retrieving relevant elements<\/li>\r\n \t<li>Injecting context into a query<\/li>\r\n \t<li>Improving response relevance<\/li>\r\n<\/ul>\r\n<strong>Module 4: Structuring a More Advanced LLM Application<\/strong>\r\n<ul>\r\n \t<li>Separating components<\/li>\r\n \t<li>Preparing a modular architecture<\/li>\r\n \t<li>Reusing chains<\/li>\r\n \t<li>Organizing application logic<\/li>\r\n \t<li>Preparing for functional scaling<\/li>\r\n<\/ul>\r\n<strong>Module 5: Advanced Use Cases<\/strong>\r\n<ul>\r\n \t<li>Document assistant<\/li>\r\n \t<li>Augmented search<\/li>\r\n \t<li>Business Q&A<\/li>\r\n \t<li>Contextualized analysis<\/li>\r\n \t<li>Assisted generation workflows<\/li>\r\n \t<li>Chaining processes around a model<\/li>\r\n<\/ul>\r\n<strong>Module 6: Best Practices and Caution<\/strong>\r\n<ul>\r\n \t<li>Architecture readability<\/li>\r\n \t<li>Maintainability<\/li>\r\n \t<li>Costs<\/li>\r\n \t<li>Document retrieval reliability<\/li>\r\n \t<li>Output validation<\/li>\r\n \t<li>Choosing LangChain when the need truly justifies it<\/li>\r\n<\/ul>","cours_recommandes":[238507,238508,238506,238512,238495],"infos_additionnelles":"<h3>LangChain, the leading framework for LLM applications<\/h3>\r\n<p>LangChain is the most widely used framework for building applications that leverage large language models. It provides abstractions that simplify the integration of models from different providers, the implementation of RAG, the creation of agents and the orchestration of complex workflows. Its flexibility and richness make it an essential tool for any AI application developer.<\/p>\r\n<p>Mastering LangChain significantly accelerates AI application development and allows you to focus on business logic rather than technical integration details.<\/p>\r\n\r\n<h3>Key components of LangChain<\/h3>\r\n<p>LangChain is built around several components. Models abstract the different model providers. Prompts allow you to build and manage prompt templates. Chains sequence operations together. Agents make dynamic decisions about which actions to execute. Retrievers manage document search for RAG.<\/p>\r\n<p>LangChain Expression Language offers a declarative syntax for building processing pipelines in a readable and maintainable way. This approach facilitates collaboration between developers and long-term application maintenance.<\/p>\r\n\r\n<h3>Implementing RAG with LangChain<\/h3>\r\n<p>RAG is one of the most in-demand patterns for enterprise AI applications. LangChain provides comprehensive tools for each step of the process: loading documents from various sources, chunking, indexing in vector databases, semantic search and injection into the model context.<\/p>\r\n<p>Advanced RAG strategies such as re-ranking, query expansion and multi-query retrieval can significantly improve result relevance. The training covers these techniques and their trade-offs.<\/p>\r\n\r\n<h3>Building intelligent agents with LangChain<\/h3>\r\n<p>LangChain agents combine a language model with tools to execute complex tasks autonomously. The agent reasons about the necessary actions, selects the appropriate tool, executes the action and adjusts its strategy based on results. This autonomy capability opens considerable possibilities for business process automation.<\/p>\r\n<p>Designing reliable and secure agents requires particular attention to control mechanisms, action limits and guardrails. The training addresses these critical aspects for enterprise deployment.<\/p>\r\n\r\n<h3>LangChain in production<\/h3>\r\n<p>Moving a LangChain prototype to a production application requires specific considerations. LangSmith offers monitoring, debugging and performance evaluation tools. LangServe simplifies the deployment of LangChain applications as APIs. These tools complement the framework to cover the entire application lifecycle.<\/p>\r\n<p>ITTA delivers this technical training in Geneva and Lausanne for developers who want to master LangChain and build professional AI applications leveraging the most powerful language models.<\/p>\r\n\r\n\r\n<h3>AI development in Switzerland, a fast-growing market<\/h3>\r\n<p>The Swiss AI application development market is experiencing sustained growth. Technology companies, startups, financial institutions and international organizations are actively seeking developers capable of building intelligent solutions. AI development skills with Python, language model APIs and frameworks like LangChain are among the most in-demand skills on the job market in French-speaking Switzerland.<\/p>\r\n<p>The presence of AWS, Google and Azure cloud regions in Switzerland facilitates the development and deployment of AI applications that comply with local data protection requirements. Developers trained on these platforms benefit from direct access to the necessary infrastructure and active technical communities in French-speaking Switzerland. This dynamic creates a favorable ecosystem for innovation and career development in the AI field.<\/p>\r\n\r\n\r\n<p>Developers trained in LangChain have a key skill for building production-quality LLM applications. They master the abstractions that simplify the integration of multiple models and the implementation of high-performance RAG pipelines. This technical expertise is highly sought after on the Swiss development market, where the ability to build sophisticated AI solutions has become a major differentiator.<\/p>\r\n\r\n<p><strong>Does LangChain work with all language models?<\/strong><\/p>\r\n<p>LangChain supports the major providers: OpenAI, Anthropic, Google, AWS Bedrock, Azure, Hugging Face and many open source models. This flexibility allows you to switch models without rewriting the application.<\/p>\r\n\r\n<p><strong>Is LangChain suitable for production applications?<\/strong><\/p>\r\n<p>Yes, many companies use LangChain in production. Complementary tools such as LangSmith and LangServe facilitate monitoring and deployment. The training covers best practices for production use.<\/p>\r\n\r\n<p><strong>Which vector database should I use with LangChain?<\/strong><\/p>\r\n<p>LangChain supports many vector databases: Chroma, Pinecone, Weaviate, Milvus, FAISS and OpenSearch. The choice depends on data volume, required performance and existing infrastructure.<\/p>\r\n\r\n<p><strong>LangChain or direct development with APIs?<\/strong><\/p>\r\n<p>LangChain is recommended for complex applications involving RAG, agents or multi-step workflows. For simple integrations, direct use of APIs may be more appropriate. The training teaches when to use each approach.<\/p>\r\n\r\n<p><strong>How do you debug a LangChain application?<\/strong><\/p>\r\n<p>LangSmith offers traceability tools that allow you to follow each step of a LangChain pipeline. Detailed logs, prompt captures and chain visualization make it easier to identify and resolve issues.<\/p>","inclus_dans_ce_cours_virtuel":"<ul>\n \t<li>Training provided by a domain expert<\/li>\n \t<li>Digital documentation and support materials<\/li>\n \t<li>Achievement badge<\/li>\n<\/ul>","inclus_dans_ce_cours_presentiel":"<ul>\n \t<li>Training provided by a domain expert<\/li>\n \t<li>Digital documentation and support materials<\/li>\n \t<li>Achievement badge<\/li>\n<\/ul>","inclus_dans_ce_cours_blended":"","inclus_dans_ce_cours_elearning":"","inclus_dans_ce_cours_surmesure":""},"yoast_head":"<!-- This site is optimized with the Yoast SEO Premium plugin v27.5 (Yoast SEO v27.5) - https:\/\/yoast.com\/product\/yoast-seo-premium-wordpress\/ -->\n<title>LLM and RAG with LangChain Training<\/title>\n<meta name=\"description\" content=\"LangChain LLM and RAG training in Geneva and Lausanne. 2 days to build RAG applications and LLM pipelines with LangChain. Labs. 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