Unlock the engineering essentials behind Retrieval-Augmented Generation (RAG) in this episode of Memriq Inference Digest — Engineering Edition. We break down the core components of RAG pipelines as detailed in Chapter 4 of Keith Bourne’s book, exploring how offline indexing, real-time retrieval, and generation come together to solve the LLM knowledge cutoff problem.

In this episode:

- Explore the three-stage RAG pipeline: offline embedding and indexing, real-time retrieval, and LLM-augmented generation

- Dive into hands-on tools like LangChain, LangSmith, ChromaDB, OpenAI API, WebBaseLoader, and BeautifulSoup4

- Understand chunking strategies, embedding consistency, and pipeline orchestration with LangChain’s mini-chains

- Discuss trade-offs between direct LLM querying, offline indexing, and real-time indexing

- Hear insider insights from Keith Bourne on engineering best practices and common pitfalls

- Review real-world RAG applications in legal, healthcare, and finance domains

Key tools & technologies:

LangChain, LangSmith, ChromaDB, OpenAI API, WebBaseLoader, BeautifulSoup4, RecursiveCharacterTextSplitter, StrOutputParser

Timestamps:

00:00 Intro & overview of RAG components

03:15 The knowledge cutoff problem & RAG’s architecture

06:40 Why RAG matters now: cost and tooling advances

09:10 Core RAG pipeline explained: indexing, retrieval, generation

12:00 Tool comparisons & architectural trade-offs

14:30 Under the hood: code walkthrough and chunking

17:00 Real-world use cases and domain-specific insights

19:00 Final thoughts & resources

Resources:

- "Unlocking Data with Generative AI and RAG" by Keith Bourne — Search for 'Keith Bourne' on Amazon and grab the 2nd edition

- Visit Memriq.ai for more AI engineering guides, research breakdowns, and tools

Thanks for listening to Memriq Inference Digest — Engineering Edition. Stay tuned for more deep dives into AI engineering topics!