AI Agents & RAG Development with Python
SU-660(3 days)
AI Agents & RAG Development with Python
15.600 DKK
(excl. VAT)
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Description
Modern LLM applications are about more than sending an instruction to a model and getting a response back. When an AI solution needs to work with your organization's own data, use external tools or complete tasks across multiple steps, areas such as context, memory, RAG, tools and workflows become central. These are also some of the most important building blocks of modern AI agents.
In this course, you build on your experience with Python and LLMs and learn to develop more advanced AI applications. Among other things, you work with dynamic context, memory and token budget management, so the model gets the right information at the right time without needlessly filling up its context window.
Next, you work with Retrieval-Augmented Generation (RAG), where a language model is combined with information from your own documents and data sources. You learn about embeddings, similarity search, document chunking and metadata, and you work with vector databases and frameworks to build solutions that can find and use relevant knowledge as the basis for the model's responses.
You also work with AI agents and agentic workflows, where models use tool calling to access functions, databases, APIs and other external tools. Using LangChain and n8n, among others, you see how an LLM can select and use relevant tools and take part in multi-step processes rather than just generating a single response. This gives you a practical understanding of how RAG, tools, context and workflow control can be combined in agent-based solutions.
As AI solutions become more complex, quality and operations become at least as important as the model itself. That is why you also work with output evaluation, groundedness, hallucinations, logging, metrics and A/B testing. Finally, we look at privacy, fallback strategies, user feedback and basic governance and compliance requirements for putting solutions into practice.
After the course, you can:
- Develop RAG solutions that combine LLMs with your own documents and data
- Work with embeddings, vector search, chunking and metadata
- Build AI agents that can use external tools and data sources
- Integrate LLMs with databases, APIs and other systems
- Work with context, memory and multi-turn interactions
- Build and control multi-step workflows with LLMs
- Evaluate the quality, relevance and groundedness of AI output
- Use logging, feedback and fallback strategies in more robust solutions
Related exam
Prerequisites
To get the most out of the course, we recommend experience or knowledge equivalent to:
Certification bundles
Module overview
- Advanced prompt engineering makes it possible to get LLMs to behave precisely and remember context
- Dynamic and contextual prompts
- Role prompting and memory management
- Token budgeting and truncation
See how you can combine document search with text generation so the AI can use your own data.
- Embeddings and similarity search using vector databases
- Document chunking and metadata tagging
- Integration with FAISS, Chroma and LlamaIndex
Build AI flows that connect different tools and steps – for example, through pipelines in LangChain or workflow tools such as n8n
- AI agents and custom chains
- Tool calling and integration with functions, databases and APIs
- Agentic workflows with LangChain and n8n
- Streaming, multi-turn interactions and workflow control
Learn methods for assessing whether an LLM's output is useful and relevant.
- Human vs. automated evaluation
- Faithfulness, groundedness and handling hallucinations
- Logging, metrics and A/B testing
Gain insight into how you can make your AI solutions more production-ready and secure.
- Privacy, logging and fallback strategies
- Collecting user feedback
- Preparing for compliance and audits
Free course advice
We care a lot about helping you find the course path that creates the most value for you and your workplace. Get in touch with our course advisors — they're ready to help you, free of charge and with no obligation.
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