And that’s a wrap. Over the past 24 days, we walked through every chapter of Laura Funderburk’s Building Natural Language and LLM Pipelines. Here are my final thoughts on the book as a whole.
This is where everything from Chapter 8 comes together. We’ve built NER pipelines, text classification tools, sentiment analyzers. Now Funderburk wires them into a multi-agent graph that can handle complex queries end to end.
After NER and text classification, Funderburk moves to the third building block: sentiment analysis. Then she starts putting all the pieces together into a multi-agent architecture. This is where the chapter gets really interesting.
Chapter 8 is where Funderburk says: enough with single pipelines. Time to build tools. And then make an agent pick which tool to use.
So I just finished reading Building Natural Language and LLM Pipelines by Laura Funderburk, and I wanted to share what I learned. This is one of those books that bridges the gap between “I can make a ChatGPT wrapper” and “I can build production AI systems that actually work.”