[{"content":" About Me # I am a student in the Datamatiker programme at Erhvervsakademi København (KEA), an Academy Profession Degree in Computer Science. I started the programme wanting to understand how software systems work — and ended up getting hooked on building them.\nMy focus is backend development, system design, and increasingly AI integrations. I am particularly interested in how to build systems that are reliable, maintainable, and actually useful in practice.\nAbout This Site # This portfolio is part of the AI-Driven Applications (AIDA) course, where the goal is to document the learning journey — not just the finished results, but also the thoughts, challenges, and insights that come up along the way.\nHere I write reflections from course sessions, document experiments, and share what I have learned about topics like LLMs, RAG, and AI integration in software.\nThe site is built with Hugo and the Blowfish theme, hosted on GitHub Pages.\nPersonal # Outside of studying, I spend time on things that keep me curious and focused. I enjoy solving problems — whether in code or elsewhere. I also like exploring new technologies, even when that means things break a little along the way.\n","date":"23 April 2026","externalUrl":null,"permalink":"/portfolio/about/","section":"Ingrid Karen Svendsen","summary":"","title":"About","type":"page"},{"content":"","date":"23 April 2026","externalUrl":null,"permalink":"/portfolio/tags/about-me/","section":"Tags","summary":"","title":"About Me","type":"tags"},{"content":"","date":"23 April 2026","externalUrl":null,"permalink":"/portfolio/tags/","section":"Tags","summary":"","title":"Tags","type":"tags"},{"content":"Here I will document my work and reflections from the course AI-driven applications.\nLoading next chapter\u0026hellip; please wait.\n","date":"17 April 2026","externalUrl":null,"permalink":"/portfolio/posts/","section":"Blog posts","summary":"","title":"Blog posts","type":"posts"},{"content":"","date":"17 April 2026","externalUrl":null,"permalink":"/portfolio/tags/chatbot/","section":"Tags","summary":"","title":"Chatbot","type":"tags"},{"content":"","date":"17 April 2026","externalUrl":null,"permalink":"/portfolio/tags/demo/","section":"Tags","summary":"","title":"Demo","type":"tags"},{"content":"","date":"17 April 2026","externalUrl":null,"permalink":"/portfolio/tags/rag/","section":"Tags","summary":"","title":"RAG","type":"tags"},{"content":" RAG - demo # Session: 3 (17/04) Topic: RAG, Chatbot\nThis is just gibberish.\nThe goal is to create a chatbot that can answere questions based on the given documents. Task: use dify.ai.\nStay tuned\u0026hellip; I need a nap first.\n","date":"17 April 2026","externalUrl":null,"permalink":"/portfolio/posts/03-rag-demo-reflection/","section":"Blog posts","summary":"","title":"Retrieval-Argumrnted Generation (RAG) demo","type":"posts"},{"content":"Computer science student at Erhvervsakademi København. Building systems, exploring AI, and getting better one commit at a time.\nThis is my working portfolio — a place to document what I build, what I learn, and what broke along the way.\n","date":"13 April 2026","externalUrl":null,"permalink":"/portfolio/","section":"Ingrid Karen Svendsen","summary":"","title":"Ingrid Karen Svendsen","type":"page"},{"content":"","date":"13 April 2026","externalUrl":null,"permalink":"/portfolio/tags/intro/","section":"Tags","summary":"","title":"Intro","type":"tags"},{"content":" Introduction to AI-Driven Applications # Session: 1 (10/04) Topic: AIDA Course introduction and portfolio setup\nAs part of the introducion to AI-driven applications, we were introduced to the concept og Large Language Models (LLMs) and its role in modern software development.\nAn LLM from what i\u0026rsquo;ve understood, is a model trained on large amounts of text data given to the machine, that is capable of predicting and generation human-like language.\nThrough this course I expect to learn how to integrate AI into software solutions and how to use LLMs as part of a structured development process. This portfolio will document that learning journey through reflections and projects.\nReferences # Course intro\nCourse lectures (Panopto)\n","date":"13 April 2026","externalUrl":null,"permalink":"/portfolio/posts/01-intro-aida-portfolio/","section":"Blog posts","summary":"","title":"Introduction to AI-Driven Applications","type":"posts"},{"content":"","date":"13 April 2026","externalUrl":null,"permalink":"/portfolio/tags/llm/","section":"Tags","summary":"","title":"LLM","type":"tags"},{"content":"","date":"13 April 2026","externalUrl":null,"permalink":"/portfolio/tags/portfolio/","section":"Tags","summary":"","title":"Portfolio","type":"tags"},{"content":" Retrieval Augmented Generation (RAG) # Session: 2 (13/04) Topic: RAG\nAs part of the course, we were introduced to Retrieval-Augmented Generation (RAG), which is a method used to improve the reliability and usefulness of Large Language Models (LLMs).\nA main limitation of LLMs is that they generate responses based on patterns in training data, which means they can produce incorrect or outdated information. This behavior is commonly referred to as hallucination.\nRAG addresses this problem by combining information retrieval with text generation. Instead of relying only on the model’s internal knowledge, relevant data is first retrieved from an external source, such as a database or document collection. This information is then provided to the model, which uses it to generate a more accurate and context-aware response.\nIn practice, this means that a system using RAG follows a pipeline where a user query is used to search for relevant documents, and those documents are then included in the prompt given to the LLM.\nA common use case for this approach is chatbot systems. Instead of relying only on general knowledge, a chatbot can retrieve relevant information from a specific data source, such as documentation or course material, and generate responses based on that. This makes the system more reliable and better suited for real-world applications.\nFrom a software perspective, this also separates the knowledge layer from the model, making the system more flexible and easier to maintain.\nReferences # Course RAG intro\nCourse lecture (Panopto)\n","date":"13 April 2026","externalUrl":null,"permalink":"/portfolio/posts/02-intro-rag-reflection/","section":"Blog posts","summary":"","title":"Retrieval-Augmented Generation (RAG)","type":"posts"},{"content":"","date":"13 April 2026","externalUrl":null,"permalink":"/portfolio/tags/setup/","section":"Tags","summary":"","title":"Setup","type":"tags"},{"content":"","externalUrl":null,"permalink":"/portfolio/authors/","section":"Authors","summary":"","title":"Authors","type":"authors"},{"content":"","externalUrl":null,"permalink":"/portfolio/categories/","section":"Categories","summary":"","title":"Categories","type":"categories"},{"content":" Projects # Here are my \u0026hellip; pause for dramatic effect\n","externalUrl":null,"permalink":"/portfolio/projects/","section":"Projects","summary":"","title":"Projects","type":"projects"},{"content":"","externalUrl":null,"permalink":"/portfolio/series/","section":"Series","summary":"","title":"Series","type":"series"}]