Building NLP and LLM Pipelines with Haystack - Book Retelling Series
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.”
The full title is a mouthful: Building Natural Language and LLM Pipelines: Build production-grade RAG, tool contracts, and context engineering with Haystack and LangGraph. Published by Packt in December 2025. ISBN: 978-1-83546-799-2.
What This Series Is About
Over the next few weeks, I’m going to walk through each chapter of this book and break it down in plain language. No academic jargon. Just the core ideas, why they matter, and how they fit together.
The book is written by Laura Funderburk, who works in developer relations at AI Makerspace. She has a math background from Simon Fraser University and experience as a data scientist and DevOps engineer. So the book comes from someone who actually builds these systems, not just talks about them.
What The Book Covers
The book is split into four parts:
Part 1: The Foundation of Reliable AI - This sets the stage. Why do most LLM applications break in production? What are data pipelines and why do they matter for AI agents? And a proper look at how large language models actually work, from attention mechanisms to context engineering.
Part 2: Building The Tool Layer with Haystack - This is where it gets hands-on. Haystack is an open-source framework by deepset for building NLP pipelines. You learn the component system, how to wire pipelines together for different use cases (indexing, RAG, hybrid retrieval), and how to build your own custom components.
Part 3: Deployment and Agentic Orchestration - Taking things from notebook experiments to production. Docker, FastAPI, CI/CD, evaluation with RAGAS, and then the big project: a multi-agent system using LangGraph with Haystack as the tool layer.
Part 4: The Future of Agentic AI - Where is all this heading? NVIDIA NIMs, Model Context Protocol (MCP), Agent-to-Agent protocols, and a final analysis of different agentic architectures with their trade-offs.
Who Should Care
If you work with LLMs and you’re tired of fragile demo code that falls apart the moment you put it in front of real users, this book is relevant. It’s aimed at NLP engineers, LLM application developers, data scientists, and technical leads who want to build AI tools that are testable, reproducible, and production-ready.
You need solid Python skills. This is not a prompt engineering guide or a no-code tutorial.
The Series Plan
Here’s what’s coming up:
- Chapter 1: NLP Pipeline Fundamentals - what they are and why agents need them
- Chapter 2: Large Language Models - how they work and what context engineering means
- Chapter 3: Haystack by deepset - the framework and its component model
- Chapter 4: Building pipelines - indexing, RAG, and hybrid retrieval
- Chapter 5: Custom components - extending Haystack for your needs
- Chapter 6: Production RAG - evaluation, reproducibility, observability
- Chapter 7: Deployment - FastAPI, Docker, Hayhooks, CI/CD
- Chapter 8: Hands-on projects - NER, classification, sentiment, and a multi-agent Yelp navigator
- Chapter 9: Future trends - hardware, MCP, A2A protocols
- Chapter 10: The architecture of agentic AI - final synthesis and trade-offs
Each chapter gets broken into digestible posts. Some bigger chapters have two or three parts.
Let’s get into it. First up: Introduction to NLP Pipelines - Part 1.
This is post 1 of 24 in the Building Natural Language and LLM Pipelines series.