As I delve deeper into the world of Retrieval-Augmented Generation (RAG), I’ve come across a wealth of resources that have genuinely transformed my understanding of how to customize large language models (LLMs). It’s an exciting area of study, especially given how rapidly it evolves and its implications in real-world applications.
If you’re embarking on a similar journey or just eager to enhance your knowledge about LLMs, I’d love to share some valuable links that have helped me immensely:
1. Understanding and Using Supervised Fine-Tuning – Cameron R. Wolfe provides a comprehensive guide on supervised fine-tuning that is a must-read for anyone looking to delve into model tuning intricacies. This post elucidates key concepts and offers practical advice to navigate through the fine-tuning process. https://cameronrwolfe.substack.com/p/understanding-and-using-supervised
2. Big LLM Architecture Comparison – Sebastian Raschka’s blog presents a detailed comparison of large language models, helping us understand their architectural differences and performance metrics. This post proves essential in discerning which model may suit your specific needs best. https://magazine.sebastianraschka.com/p/the-big-llm-architecture-comparison
3. Fine-tuning Anthropic’s Claude 3 in Amazon Bedrock – This AWS blog post provides insights on boosting model accuracy using Claude 3. The hands-on approach laid out here for fine-tuning models in Amazon Bedrock has been particularly useful for practical applications. https://aws.amazon.com/blogs/machine-learning/fine-tune-anthropics-claude-3-haiku-in-amazon-bedrock-to-boost-model-accuracy-and-quality/
4. GPT Instruction Fine-tuning Code – For those who love diving into the code, this GitHub repo by Raschka provides the main chapter code for fine-tuning GPT models. It’s an excellent resource for both understanding and implementing the theory behind it. https://github.com/rasbt/LLMs-from-scratch/blob/main/ch07/01_main-chapter-code/gpt_instruction_finetuning.py
5. Appendix on Fine-tuning LLMs – Another valuable resource from Raschka’s repo is a detailed Jupyter Notebook that breaks down the concepts further, ensuring you have both the theoretical and practical aspects at your fingertips. https://github.com/rasbt/LLMs-from-scratch/blob/main/appendix-E/01_main-chapter-code/appendix-E.ipynb
6. Instacart’s Elasticsearch and Postgres Integration – For a real-world application of how businesses can utilize advanced databases with AI, this InfoQ article discusses how Instacart effectively implements Elasticsearch with Postgres. https://www.infoq.com/news/2025/08/instacart-elasticsearch-postgres/
Moreover, my experience following the guide on building an LLM from scratch has provided me with a robust understanding of the underlying architecture. Understanding the components and how they fit into the bigger picture is crucial when you venture into customizing and optimizing LLMs.
Through RAG and personalization of these models, I’m excited about the innovative applications that lie just ahead. Stay tuned for more updates as I progress through this fascinating realm!
I've reviewed the blog entry at [here](https://techbychris.com/?p=263). Based on my evaluation: - **Human contribution (~80%):** The introduction, the personal touch ("As I delve deeper..."), the explanation of why these links are useful, and the closing ("If you need any further assistance or modifications...") reflect human input. - **AI contribution (~20%):** While the list of links provided is likely from Chris's input, the way they're presented ("https://...", "https://...") is more structured and impersonal, suggesting AI involvement.