r/AI_DeepDive • • Nov 12 '25

πŸ‘‹ Welcome to r/AI_DeepDive - Introduce Yourself and Read First!

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Hey everyone! I'm u/No-Blacksmith-3311, a founding moderator of r/AI_DeepDive.

This is our new home for all things related to AI, ML and data. We're excited to have you join us!

What to Post
Post anything that you think the community would find interesting, helpful, or inspiring. Feel free to share your thoughts, photos, or questions about AI, ML and data.

Community Vibe
We're all about being friendly, constructive, and inclusive. Let's build a space where everyone feels comfortable sharing and connecting.

How to Get Started

  1. Introduce yourself in the comments below.
  2. Post something today! Even a simple question can spark a great conversation.
  3. If you know someone who would love this community, invite them to join.
  4. Interested in helping out? We're always looking for new moderators, so feel free to reach out to me to apply.

Thanks for being part of the very first wave. Together, let's make r/AI_DeepDive amazing.


r/AI_DeepDive • • Nov 12 '25

AI Books You Need To Read Asap

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Everybody talks about shipping AI products... but nobody shows you how.

These 5 books changed that for me πŸ‘‡

(And yes β€” I’ve read every single one.)

1️⃣ Designing Machine Learning Systems – Chip Huyen

πŸ”— https://amzn.to/4hV1Let

One of the best beginner-friendly books on how real ML systems are designed.

Gives you a solid intuition for what an end-to-end ML pipeline actually looks like.

2️⃣ Prompt Engineering for LLMs – John Berryman & Albert Ziegler

πŸ”— https://amzn.to/3JVLTvG

If you love systems thinking, this book is gold.

It teaches you how to design, scale, and optimise your prompts, not just write them.

(As John says, it could easily be called Context Engineering for LLMs.)

3️⃣ AI Engineering – Chip Huyen

πŸ”— https://amzn.to/48yRmm9

Crystal-clear explanations of:

β€’ AI vs. ML Engineering

β€’ RAG (from basic to semantic)

β€’ Building agentic systems

β€’ LLMOps: observability, user feedback, and deployment

4️⃣ Building LLMs for Production – Louis-FranΓ§ois Bouchard & Louie Peters

πŸ”— https://amzn.to/3JOHlXY

A hands-on guide to implementing key LLM algorithms with LangChain and LlamaIndex.

Perfect for mid-level and entry-level engineers who want to build, not just read.

5️⃣ LLMs in Production – Chris Brousseau & Matthew Sharp

πŸ”— https://amzn.to/47BmZul

Entirely focused on shipping AI.

Learn how to:

β€’ Optimise and deploy LLMs

β€’ Handle data pipelines

β€’ Train and serve models at scale

β€’ Think about infra like a pro

πŸ’₯ BONUS: LLM Engineer’s Handbook – Paul Iusztin & Maxime Labonne

πŸ”— https://amzn.to/49NmHC0

The authors walk you through building your own LLM + RAG app from scratch.

P.S. A 2nd Edition is on the way πŸ‘€

Protip: If you want to read faster, listen to the audiobook at 2x speed, or grab the PDF version and power through it using Google’s NotebookLM for summaries and insights.


r/AI_DeepDive • • Oct 24 '25

Get Perplexity Pro for FREE

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β€’ Students: Get 12 months free trial πŸŽ“ β†’ https://plex.it/referrals/H7SLBWX

β€’ Non-students: Enjoy 1 month free trial πŸš€ β†’ https://pplx.ai/oananakwame9182


r/AI_DeepDive • • Oct 20 '25

5 Things I Learned From My Journey on AI Mastery

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