Insights

This post shows you how to build an agent using Amazon Bedrock. Everything is deployed with CDK code; the sample code is available through GitHub.
A widespread use of large language models (LLMs) is creating agents. An agent can reason, decide what to do, and use tools to take action.

Data has always been at the heart of innovation, driving decisions, strategies, and technologies. And now we stand at the beginning of a yet another new era that is defined by advanced data sharing solutions based on data spaces.

RAG, short for Retrieval Augmented Generation, is a popular pattern for working with Large Language Models. In previous blog posts, I discussed the importance of chunking in combination with retrieval strategies for RAG. In another blog post, I wrote about semantic chunkers.

Make or buy? That's a question asked often when it comes to software development. Do we make everything ourselves or do we just buy a third party library? Most of the time the decision is to buy, but is this always the best choice?

In this blog, I take you on our journey from a library to a custom

One often (over) used feature of lexical search is synonyms. If something does not work, we add a synonym. When used correctly, synonyms are a critical feature. Still, they were so annoying to use. I wrote multiple synonym management components, where synonyms were stored in a database and then prov

DALL·E generated image showing the flow for semantic chunks

In a previous blog post, I wrote about providing a suitable context for an LLM to answer questions using your content. The previous post talks about having the proper chunking mechanism and matching the semantics of your question or state