Ideas, experiments, project notes, and practical work in AI & Data.
Shannon HubLarge Language Models are good at answering questions, generating text, and reasoning about information. But many real-world tasks require more than generating an answer. An AI system may…
Shannon HubRAG and fine-tuning are sometimes treated as competing approaches. But in practice, they can solve different parts of the same problem. The real question is not which one is better. Inste…
Shannon HubWhen building an LLM application, there is usually a point where prompt engineering is no longer enough. You may need the model to know information it has never seen before. Or you may wa…
Shannon HubA real RAG system also needs to decide which information should actually be sent to the LLM. This is where RAG becomes an engineering problem rather than simply an LLM problem. ## From Re…
Shannon Hub## How RAG Works: From Documents to Better LLM Answers Large Language Models are powerful, but they have an important limitation that they do not automatically know your private or up-to-…
Shannon HubSo far, we have built an LLM application with several parts. RAG gives the model external knowledge. Tools allow it to interact with external systems. But what happens when the LLM can de…
Shannon HubImagine your company has 10,000 documents. They might include product manuals, company policies, research reports, customer support documents and other internal knowledge. Now a user asks…
Shannon HubAn LLM is very good at understanding and generating language. But what if we want it to do something outside the model? For example: What is the current price of Product A? The LLM should…
Shannon HubWhen people first start working with Large Language Models (LLMs), it is easy to think that an AI application is simply: User → LLM → Answer. For a basic chatbot, this may be enough. But …