How to Start with AI Engineering in end of 2025

A Clear, Practical Hands-on Roadmap

Hey there👋 ,

Welcome to a special edition of “The Mother of AI Project” series.

If you’ve been thinking about getting serious with AI engineering but were unsure where to start or what to build, this is the guide you read, save, and keep referring back to.

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Where Should You Actually Start With AI Engineering?

The biggest challenge for most learners isn’t lack of resources, it’s lack of order.

Most people start by jumping between:

• LLM tutorials

• Transformers articles

• LangChain experiments

• vector database videos

• fine-tuning guides

• random open-source agents

• too-early MLOps content

This leads to confusion, burnout, and zero end-to-end systems.

So here’s the direct, honest answer:

The strongest, most efficient starting point for AI engineering today is RAG.

Not Transformers.
Not LangChain.
Not prompting.
Not Agents.

RAG - Retrieval-Augmented Generation.

Let’s break down why.

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Why Start with RAG (and Not LLMs or Agents)?

RAG forces you to learn AI the way it is practiced in real companies: through systems, infrastructure, retrieval, indexing, and deployment. This makes it the highest-leverage entry point.

Here are the key reasons.

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1. RAG teaches the real engineering layer of AI systems.

LLM usage alone mostly teaches how to write prompts and manage tokens.

A proper RAG system forces you to understand:

• Ingestion and parsing

• ETL pipelines

• OCR and PDF processing

• Embeddings and indexing

• Hybrid search (BM25 + vectors)

• FastAPI service layers

• Dockerized microservices

• Chunking strategies

• Caching

• Observability and logging

• Container orchestration

• Deployment and environment management

• Real debugging under constraints

This is what companies hire AI engineers for.

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2. RAG is fundamentally an Information Retrieval problem - which teaches Search + Recommendations.

This is the part no one tells beginners:

RAG is built on the same foundations as:

• Google Search

• YouTube recommendations

• Amazon and Netflix ranking

• Enterprise document search

• Internal knowledge systems

• Content recommendation engines

By building RAG systems, you learn:

• how queries are interpreted

• ranking and scoring (BM25, vector similarity)

• metadata filtering

• semantic similarity

• hybrid retrieval

• chunk-level relevance scoring

• evaluation metrics like nDCG-style thinking

This directly prepares you for:

• search engineering

• retrieval pipelines

• recommendation systems

• personalized AI

• hybrid ranking systems

RAG quietly builds the foundations of two of the most valuable applied ML domains.

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3. RAG naturally exposes you to LLMs, Transformers, MLOps, and Agents - through real use-cases.

You learn:

• how LLMs hallucinate

• how context windows behave

• how embeddings are generated

• intuition behind attention and tokenization

• pipeline orchestration

• error handling and fallbacks

• monitoring and logging

• multi-step reasoning workflows

Not theoretically! but through actual systems you build.

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4. Most real AI products today follow this sequence:

Search → RAG → Agentic Layer

Across enterprise AI products:

• knowledge assistants

• customer support systems

• internal research tools

• document intelligence

• domain-specific chat systems

• enterprise copilots

Many rely on RAG at their core.

Mastering RAG means you are aligned with the modern AI stack.

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5. RAG enables real, portfolio-worthy systems - fast!

Within weeks, you can build:

• a private local assistant

• a document search engine

• a semantic Q&A system

• a research assistant over academic papers

• a Telegram AI bot with RAG backend

• a production-style retrieval system

These are meaningful, career-changing projects.

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Your AI Engineering Roadmap for 2025
(Entering 2026 Strong)

Assuming you know Python, here is the exact path we recommend.

Clear, practical, and aligned with real-world systems.

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Step 1 - Understand the Fundamentals of RAG (2 hours)

Build the mental model:

• why LLMs hallucinate

• why retrieval fixes this

• keyword search vs vector search

• how hybrid search works

• what “context” truly means

• how a RAG pipeline flows end-to-end

Start here:

RAG systems simplified!

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Step 2 - Build Your First Local Private RAG (3 days)

This is your first complete system.

It teaches nearly everything you need at an entry level:

• parsing your own PDFs

• OCR with fallback

• embedding generation

• chunking and segmentation

• OpenSearch hybrid retrieval

• local LLM inference with Ollama

• a Streamlit UI

• Dockerized setup

Use:

• Part 1:

  Build a Local LLM-based RAG System for Your Personal Documents - Part 1

• Part 2:

  Build a Local LLM-based RAG System for Your Personal Documents - Part 2

• Repository: https://github.com/jamwithai/local-rag-system

  • With Notebooks and detailed explanations of:

    • 01_Prerequisites_and_Environment_Setup.ipynb

    • 02_OpenSearch_Index_and_Ingestion_standalone.ipynb

    • 03_Hybrid_Search_and_Retrieval.ipynb

This one project already puts you ahead of 90% of beginners.

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Step 3 - Build a Production-Grade RAG Pipeline (6 weeks)

A structured 6-week path that mirrors real engineering work

This is your first serious system:

• ingestion via Airflow

• arXiv API client with rate limiting

• Docling-powered PDF parsing

• metadata storage (PostgreSQL)

• content indexing (OpenSearch)

• BM25 + vector + hybrid retrieval

• Jina embeddings

• section-aware chunking

• FastAPI backend

• Gradio interface

• Langfuse observability

• Redis caching

Start here:

The Infrastructure That Powers RAG Systems

Code:

• https://github.com/jamwithai/arxiv-paper-curator

Breakdown:

• Week 1 - Infrastructure

• Week 2 - Data ingestion

• Week 3 - Search foundations

• Week 4 - Chunking + hybrid retrieval

• Week 5 - Full RAG + LLM

• Week 6 - Monitoring + caching

This prepares you for the systems companies are building today.

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Step 4 - Move to Advanced Territory: Agentic RAG (2 days)

Once you master RAG, you can step into intelligent systems:

• query validation

• document grading

• multi-step retrieval

• intelligent fallback strategies

• LangGraph orchestration

• Telegram integration

• adaptive search strategies

Start with:

Agentic RAG with LangGraph & Telegram (with Video explanation)

This is where you start working like an advanced AI engineer.

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Final Thoughts - Use the End of 2025 Intentionally

We are at the end of 2025.

This is the moment where many people reflect on their year and decide what to change in the next one.

You have two options:

• Keep consuming scattered content, waiting for clarity to appear someday

  or

• Commit to one clear, structured path that genuinely builds your ability to design and ship AI systems

You do not need to learn everything.
You do not need perfect math.
You do not need a research background.

You need:

• one strong starting point

• one real system

• one direction

• consistent building

RAG gives you that foundation.

It teaches search, retrieval, recommendations, embeddings, system design, LLM integration, monitoring, caching, and agent readiness - all in one integrated journey.

If you start this path now, by beginning of 2026 you can be a completely different engineer.

And then you will continue with us on AI Agent journey!!!

Your future self will thank you for making this decision at the right time.

Let’s build with intention 💪

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