Everything will be 200 OK :)

     Welcome back, In Part 9 of the series we had a look at using tool-calling as a means to perform custom business logic functions and presenting the business data obtained from there to LLM so that it may have the capability to respond to prompts more contextually aimed towards a particular business use case. We continue our journey on the pre-trained to specific date shortcomings of a LLM and see how we can adapt solution further where we address the problem of presenting our documents to it using Retrieval Augmented Generation (RAG) , so that we can empower end user with information from their prompts about it. Excited ? Let's get into it. Retrieval Augmented Generation (RAG) - Theory     RAG can be summarized as a two step process. In the first step, we store our information content through a custom ETL process into a special kind of persistence store named Vector Store where each chunk is stored as a series of multi dimension N dimension vectors throug...

    Welcome back, from wherever you are, I hope you've had a lovely week. One of the caveats that comes with using LLM is that they are pre-trained on information up to a certain date. Furthermore, they would not be able to provide information on custom business data unless trained to do so. In this post we look to address the latter, where we present our custom data (static, obtained from third party vendor, database, etc) to LLM through means of custom business functions termed tool-calling so that it may response accordingly to relevant prompts made by user. It is to be noted, that not all LLM's support tool calling. Capabilities of different providers and their offerings can be found here . Tool-Calling Theory       Tool-Calling is one of many options (others being RAG, MCP, Agentic AI) that will follow in the series of posts that follow. Before we dive deep into those, understanding tool-calling would give a basic idea of how the solution is conceptualized...