I taught myself how to build RAG + AI Agents in production.

Been running them live for over a year now. Here are 4 steps + the only resources you really need to do the same.

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Ugly truth: most “AI Engineers” shouting on social media haven’t built a single real production AI Agent or RAG system.

If you want to be different - actually build and ship these systems: here’s a laser-focused roadmap from my own journey.

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🚀 𝗦𝘁𝗮𝗿𝘁 𝘄𝗶𝘁𝗵 𝗳𝘂𝗻𝗱𝗮𝗺𝗲𝗻𝘁𝗮𝗹𝘀

Because no matter how fast LLM/GenAI evolves, your ML & software foundations keep you relevant.

✅ Hands-On ML with TensorFlow & Keras: lnkd.in/dWrf5pbS

✅ ISLR: lnkd.in/djGPVVwJ

✅ Machine Learning for Beginners by Microsoft (free curriculum):

lnkd.in/d8kZA3es

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1️⃣ 𝗠𝗮𝘀𝘁𝗲𝗿 𝗟𝗟𝗠𝘀 & 𝗚𝗲𝗻𝗔𝗜 𝗦𝘆𝘀𝘁𝗲𝗺𝘀

→ Learn to build & deploy LLMs, understand system design tradeoffs, and handle real constraints.

📚 Must-reads:

✅ Designing ML Systems – Chip Huyen: lnkd.in/guN-UhXA

✅ The LLM Engineering Handbook – Iusztin & Labonne: lnkd.in/gyA4vFXz

✅ Build a LLM (From Scratch) – Raschka: lnkd.in/gXNa-SPb

✅ Hands-On LLMs GitHub: lnkd.in/eV4qrgNW

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2️⃣ 𝗚𝗼 𝗯𝗲𝘆𝗼𝗻𝗱 𝘁𝗵𝗲 𝗵𝘆𝗽𝗲 𝗼𝗻 𝗔𝗜 𝗔𝗴𝗲𝗻𝘁𝘀

→ Most demos = “if user says hello, return hello.”

Actual agents? Handle memory, tools, workflows, costs.

✅ AI Agents for Beginners (GitHub): lnkd.in/eik2btmq

✅ GenAI Agents – build step by step: lnkd.in/dnhwk75V

✅ OpenAI’s guide to agents: lnkd.in/guRfXsFK

✅ Anthropic’s Building Effective Agents: lnkd.in/gRWKANS4

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3️⃣ 𝗥𝗔𝗚 𝗶𝘀 𝗻𝗼𝘁 𝗷𝘂𝘀𝘁 𝗮 𝘃𝗲𝗰𝘁𝗼𝗿 𝗗𝗕

Real Retrieval-Augmented Generation requires:

→ Chunking, hybrid BM25 + vectors, reranking

→ Query routing & fallback

→ Evaluating retrieval quality, not just LLM output

✅ RAG Techniques repo: lnkd.in/dD4S8Cq2

✅ Advanced RAG: lnkd.in/g2ZHwZ3w

✅ Cost-efficient retrieval with Postgres/OpenSearch/Qdrant

✅ Monitoring with Langfuse / Comet

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4️⃣ 𝗚𝗲𝘁 𝘀𝗲𝗿𝗶𝗼𝘂𝘀 𝗼𝗻 𝗦𝗼𝗳𝘁𝘄𝗮𝗿𝗲 & 𝗜𝗻𝗳𝗿𝗮

→ FastAPI, async Python, Pydantic

→ Docker, CI/CD, blue-green deploys

→ ETL orchestration (Airflow, Step Functions)

→ Logs + metrics (CloudWatch, Prometheus)

✅ Move to production: lnkd.in/dnnkrJbE

✅ Made with ML (full ML+infra): lnkd.in/e-XQwXqS

✅ AWS GenAI path: lnkd.in/dmhR3uPc

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5️⃣ 𝗪𝗵𝗲𝗿𝗲 𝗱𝗼 𝗜 𝗹𝗲𝗮𝗿𝗻 𝗳𝗿𝗼𝗺?

→ Stanford CS336 / CS236 / CS229 (Google it)

→ MIT 6.S191, Karpathy’s Zero to Hero: lnkd.in/dT7vqqQ5

→ Google Kaggle GenAI sprint: lnkd.in/ga5X7tVJ

→ NVIDIA’s end-to-end LLM stack: lnkd.in/gCtDnhni

→ deeplearning.ai’s short courses: lnkd.in/gAYmJqS6

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💥 𝗞𝗲𝗲𝗽 𝗶𝘁 𝗿𝗲𝗮𝗹:

Don’t fall for “built in 5 min, dead in 10 min” demos.

In prod, it’s about latency, cost, maintainability, guardrails.

♻️ Let's repost to help more people on this journey 💚