AI Engineering Fundamentals

Essential principles and practices for building production-ready AI systems, from evaluation strategies to structured implementation approaches.

Similarities: AI Engineering vs Software Engineering

AI engineering is closer to Software Engineering than ML engineering. In both AI and Software engineering, the emphasis is on the product and the model is more of an implementation detail.

AI Engineering Basics

Evaluation

Comprehensive evaluation needs to cover both approaches:

🤖 Automated

Systematic testing with metrics, benchmarks, and automated validation pipelines.

👥 Manual

Direct user feedback and conversations - listed as the highest value piece.

Using RAG

Understanding the pros and cons between using:

🔍 Keyword Search

Traditional exact matching, fast and precise for known queries

🧠 Vector Store

Semantic similarity, better for conceptual understanding

Finetuning

Used as a last resort because of:

• Hosting overhead - requires dedicated infrastructure
• Pace of model development - often erodes finetuned advantages quickly

Structured Approach

🚀 Start with Human-in-the-Loop

Begin with humans in the loop and test the places where GenAI can make a high impact. (Clue: it's often not "everywhere.")

📊 Record Process Data

Systematically record data from the human processes to inform AI integration decisions.

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