AI Engineering Guides
Practitioner guides covering AI engineering, generative AI, RAG, agentic systems, prompts, and careers.
What is AI Engineering?
AI engineering as a discipline: scope, responsibilities, the modern stack, and how it differs from data science and ML research.
Updated 2026-06-22
Generative AI Explained
How generative models work, why transformers won, the role of pre-training and post-training, and where generative AI fits in real products.
Updated 2026-06-22
Agentic AI Systems Guide
Designing agentic systems with tool use, planning, memory, and safety. Patterns, failure modes, and evaluation.
Updated 2026-06-22
Retrieval-Augmented Generation (RAG) Guide
End-to-end RAG: chunking, embeddings, retrieval strategies, ranking, prompt assembly, evaluation, and operations.
Updated 2026-06-22
Prompt Engineering Best Practices
Prompt design patterns, structured outputs, evaluation, and how to manage prompts in production.
Updated 2026-06-22
AI Agent Architecture Patterns
Reusable architectures for AI agents: router, plan-and-execute, ReAct, supervisor, and graph-based multi-agent systems.
Updated 2026-06-22
Vector Databases Explained
What vector databases do, how ANN indexes work, when to use a vector DB vs Postgres pgvector, and how to keep recall honest.
Updated 2026-06-22
LLM Application Development
From prototype to production: structuring code, managing prompts, retrieval, evaluation, deployment, and observability.
Updated 2026-06-22
AI Safety and Governance
Practical safety for production AI: input/output controls, evaluations, privacy, abuse prevention, and governance practices.
Updated 2026-06-22
Future Careers in AI Engineering
Emerging AI engineering roles, the skill ladder from junior to staff, and how HIGAET Academy maps to industry hiring tracks.
Updated 2026-06-22