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.
An advanced training course to move from simple prototypes to orchestration logic
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.
Module 1: Discovering LangChain and Its Positioning
Module 2: Building Processing Chains
Module 3: Managing Context and Document Sources
Module 4: Structuring a More Advanced LLM Application
Module 5: Advanced Use Cases
Module 6: Best Practices and Caution
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.
Mastering LangChain significantly accelerates AI application development and allows you to focus on business logic rather than technical integration details.
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.
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.
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.
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.
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.
Designing reliable and secure agents requires particular attention to control mechanisms, action limits and guardrails. The training addresses these critical aspects for enterprise deployment.
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.
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.
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.
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.
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.
Does LangChain work with all language models?
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.
Is LangChain suitable for production applications?
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.
Which vector database should I use with LangChain?
LangChain supports many vector databases: Chroma, Pinecone, Weaviate, Milvus, FAISS and OpenSearch. The choice depends on data volume, required performance and existing infrastructure.
LangChain or direct development with APIs?
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.
How do you debug a LangChain application?
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.
Monday to Friday
8:30 AM to 6:00 PM
Tel. 058 307 73 00
ITTA
Route des jeunes 35
1227 Carouge, Suisse
Monday to Friday, from 8:30 am to 06:00 pm.
