Context and Similarity - Food for Thought

In GenAI Java applications, “context” is critically important for guiding LLMs to generate useful results. You can include context in a simple prompt, or context can be extracted from a data store, eg, in a RAG-based system. Context is what separates a chatbot that sounds smart from one that is actually helpful. It allows agents to make more accurate decisions on what actions to take. Control of your context also allows black hats to distort results for nefarious purposes.

This short session breaks down context into similarity and embeddings. You’ll learn the fundamentals of embeddings, how embeddings turn text into vectors, how similarity search finds the best matching chunks, and what your Java code is really doing when it chunks documents, queries a vector store, and selects results to feed the LLM. We will cover the practical knobs that matter, including chunk size and overlap, metadata filters, distance metrics, and top-k settings, and how each one impacts answer quality, latency, and the risk of hallucinations. You will also see why adding more context and large “context windows” are not always better, and how to focus on the right context instead.