5 Projects To Get Started With AI Engineering

2026-06-16 · Source: Artificial Intelligence in Plain English - Medium · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Software Development & Engineering · Depth: Novice, quick

Summary

The article, published on March 29, 2026, introduces five beginner-friendly projects designed to help individuals enter the applied AI engineering field. It addresses the common issue where many tutorials are either overly theoretical or immediately delve into complex topics like multi-agent systems, memory, and orchestration, which can overwhelm newcomers. The author advocates for hands-on learning through small projects as the most effective way to grasp concepts such as prompting, LLM behavior, data handling, APIs, hallucinations, vector databases, retrieval, and context windows. These projects are presented in increasing order of difficulty, starting with building a simple prompt-based application, and are intended to provide practical understanding of modern AI product development.

Key takeaway

For AI Students or aspiring AI Engineers struggling with complex tutorials, prioritize hands-on project building. Start by developing a simple prompt-based application to master fundamental concepts like prompting and LLM behavior. This practical approach will provide a solid foundation in data handling, APIs, and retrieval, preparing you for more advanced topics like agent workflows and vector databases, ultimately accelerating your entry into applied AI development.

Key insights

Building small, practical projects is the most effective way to learn applied AI engineering concepts.

Principles

• Hands-on projects teach core AI concepts better than tutorials.
• Start with simple prompting before complex systems.
• Gradually increase project difficulty for effective learning.

Method

Learn applied AI by building small projects, starting with simple prompt-based applications and progressively moving to more complex systems like RAG or agents.

In practice

• Build a simple prompt-based application first.
• Focus on understanding LLM behavior and APIs.
• Explore vector databases and retrieval in projects.

Topics

• AI Engineering
• Prompt Engineering
• Large Language Models
• Project-Based Learning
• Applied AI Development
• RAG Systems

Best for: AI Engineer, AI Student

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Editorial summary, takeaway, and curation by AIssential. Original article published by Artificial Intelligence in Plain English - Medium.