YouTube video: https://youtu.be/JV3pL1_mn2M?si=JkfzXAMWUvKbDr1n

This document distills key insights and lessons from the book AI Engineering by Chip Win, summarizing critical aspects of this rapidly evolving, high-demand field offering lucrative career opportunities. It covers foundational models, prompt engineering, retrieval augmented generation (RAG), agents, fine-tuning, dataset curation, inference optimization, system architecture, and user feedback integration.

1. What is AI Engineering?

AI engineering focuses on building applications leveraging large pre-trained foundation models instead of training models from scratch, contrasting with traditional machine learning approaches.

• Foundation models are enormous AI systems (e.g., GPT by OpenAI, PaLM by Google) pre-trained using self-supervised learning on large unlabelled datasets.
• These models have drastically reduced the barrier to creating AI-powered applications while improving capabilities, causing explosive growth in AI engineering.
• AI engineering emphasizes adapting and integrating these models to real-world problems rather than training them from zero.

2. Foundation Models

Training and Data

• Foundation models learn self-supervised by predicting parts of their input, bypassing human labeling bottlenecks.
• These models often train on vast web-crawled datasets, introducing:
  • Biases (e.g., predominance of English data)
  • Misinformation, clickbait, toxic content risks
• OpenAI, for instance, filters training data by quality (e.g., Reddit links with upvotes).

Architectures

• The Transformer architecture dominates foundation models due to its attention mechanism, enabling parallel processing and focusing on relevant input tokens.
• Traditional sequence-to-sequence models used encode-decode sequential token processing, which was slow and limited.
• Transformers utilize:
  • Query vectors (Q)
  • Key vectors (K)
  • Value vectors (V)
• Attention scores Q and K to weigh V influence on outputs, enabling flexible long-context understanding.