When AI talk starts sounding too magical, it helps to return to the algorithms that machine behavior is actually built on.
The chapter connects search, heuristics, evolutionary methods, learning, and decision-making to the intuitions you need before modern LLM and GenAI systems can be judged realistically.
In interviews, it helps you separate the product layer from the algorithmic foundation and explain more precisely where a system's strengths and weaknesses come from.
Practical value of this chapter
Design in practice
Translate guidance on AI algorithm foundations and model selection for product tasks into architecture decisions for data flow, model serving, and quality control points.
Decision quality
Evaluate system quality through both model and platform metrics: precision/recall, latency, drift, cost, and operational risk.
Interview articulation
Frame answers as data -> model -> serving -> monitoring, showing where constraints appear and how you manage them.
Trade-off framing
Make trade-offs explicit for AI algorithm foundations and model selection for product tasks: experiment speed, quality, explainability, resource budget, and maintenance complexity.
Source
Telegram: book_cube
Part 1 of the review with chapters 1-6.
Grokking Artificial Intelligence Algorithms
Authors: Rishal Hurbans
Publisher: Manning Publications, 2020
Length: about 350 pages
An introductory guide to AI algorithms: search, evolutionary methods, swarm intelligence, ML, ANN, and Q-learning. Best read together with modern LLM/GenAI sources.
What this book is about
This is an accessible introduction to classical artificial intelligence algorithms: from search and evolutionary approaches to ML basics, ANN, and reinforcement learning.
The main limitation today: the book predates the widespread LLM/GenAI engineering wave, so it should be combined with more recent sources.
Structure: chapters 1-6
What is artificial intelligence
AI definition, short historical context from 1956, and the difference between narrow AI and AGI.
Search fundamentals
Core search algorithms: binary search, BFS, and DFS, plus intuition for algorithmic efficiency.
Smart search
Informed search (A*) and adversarial algorithms (min-max, alpha-beta pruning).
Evolutionary algorithms
Genetic algorithms: selection, mutation, crossover, and progressive population improvement.
Advanced evolutionary algorithms
Genetic and evolutionary programming, encoding strategies, and optimization scenarios.
Swarm intelligence: ants
Ant colony algorithms and pheromone trails as a strategy for route optimization.
Structure: chapters 7-10
Swarm intelligence: particles
Particle Swarm Optimization: particle movement through the search space using local and global experience.
Machine learning
Supervised, unsupervised, and reinforcement learning; regression, classification, and clustering tasks.
Artificial neural networks
ANN foundations: layers, forward pass, and training with backpropagation.
Reinforcement learning with Q-learning
Q-function, reward, action choice, and practical framing through MDP-style tasks.
Related chapter
Hands-On Large Language Models
The next step after a classical AI foundation.
How to read this book in 2026
The book covers fundamental AI algorithms well, but has little to no coverage of transformers, LLMs, and GenAI patterns.
Treat it as a first step: get strong basics in search/evolution/swarm/ML first, then move to modern LLM-oriented resources.
It may feel too basic for senior readers, but it is excellent for leveling shared terminology in a team.
Related chapters
- Why engineers should know ML and AI - Provides the AI/ML section overview and positions this book in the broader learning path.
- Deep Learning and Data Analysis: A Practical Guide (short summary) - Builds on Grokking fundamentals with more applied ML and deep learning practice.
- Precision and Recall Basics - Reinforces core quality metrics used to evaluate classification-oriented algorithms.
- AI Engineering (short summary) - Shows the next step from classical algorithms toward production AI system practices.
- Hands-On Large Language Models (short summary) - Bridges classical AI foundations to modern LLM, embeddings, and RAG workflows.