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ADAGIA 101-2025.05 
Artificial Intelligence: Introduction on challenges concerning LLM-based AI-tools development
Peter Kaczmarski and Fernand Vandamme
https://doi.org/10.57028/LWT-101-Z1072 


Pages: 104        
E-ISSN: 2953-1489
Order form		         Book Summary
In recent years there have been many breakthroughs in various areas of Artificial Intelligence computing technologies, mainly with respect to building general GPTs (Generative Pre-Trained Transformers), and their extensions. This book takes a snapshot of today’s state-of-the-art in the main areas of these developments from the perspective of available AI capabilities and their possible utilization in custom cloud-based AI- tools. In the first two parts, attention is given to online accessibility of the top-ranked Large Language Models (LLMs) from Anthropic, Google, and OpenAI, and to the assessment of their available capabilities such as streaming chat, Theory of Mind tests analysis, reasoning on images, and LLM-based information retrieval using RAG, while the third part highlights the path from creating a custom local AI-tool to its cloud deployment and further to its online use. On a more general level, the presented insights and code snippets form a reference for building more extensive Artificial Intelligence use cases using Python and/or .NET 8 (LTS)


Table of contents

About this book    4
1 LLM-based AI-tools: case studies    8
1.1 Introduction    8
1.2 LLM-based AI-tools for knowledge assistance   10
1.3 Adapting the answering style of LLMs 14
1.4 LLM-based scenario analysis and Theory of Mind 17  17  
1.5 Document summarisation  19
1.6 Advanced use cases for LLMs  21
1.7 Conclusions 29
1.8 Appendix A: Developing LLM chat clients in Python 30
1.9 Appendix B: Chat test results overview 34
1.10 Appendix C: Theory of Mind test 40
1.11 Appendix D: Document summarisation tests 42
1.12 References 48 
2 LLM-based text search using RAG: implementation and assessment 52
2.1 Introduction 52
2.2 RAG procedure summary 53
2.3 Implementation overview 54
2.4 Experimental results  56
2.5 Discussion of limitations and challenges 59
2.6 Conclusions  60 
2.7 Appendix A: Python RAG implementation  61 
2.8 Appendix B: Test document contents  64
2.9 References  69
3 Building LLM-based AI-tools for the cloud 72 
3.1 Introduction  72 
3.2 Implementing a Blazor OpenAI LLM Chat in C#  73 
3.2.1 Initial Blazor project 73 
3.2.2 Adding a Razor chat component 74 
3.2.3 Implementation of the LLM chat functionality 76
3.2.4 Testing the chat function in debug mode  81 
3.3 Deploying the chat application to Google Cloud Platform 83 
3.3.1 Publishing a release version of the web application 83 
3.3.2 Docker image creation 85 
3.3.3 Importing Docker image to Google Artifact Registry  86
3.3.4 Securing the OpenAI API key in GCP Secret Manager 88
3.3.5 Deploying Docker image from GAR to GCP Cloud Run  89 
3.4 User experience: a simple test  91
3.5 Conclusions  94
3.6 Appendix A: Code listing (llmchat.razor)  95
3.7 References 98
4 Summary  102
About the authors  104