Ai_ml terms

9 terms of 1466 total

🤖 Patterns, models, and agents rewriting what software can do

Machine learning and AI are reshaping what software can do and how it is built. This category covers model architectures, training concepts, inference patterns, vector databases, RAG pipelines, agent frameworks, and the terminology you need to work intelligently alongside — or build on top of — modern AI systems. Understanding these concepts is increasingly a core developer skill.

More on AI / ML history · 6 references · key concepts

History

AI and machine learning emerged from theoretical computer science in the 1950s, with early symbolic AI systems attempting to encode human reasoning explicitly. The field shifted substantially after the 1980s AI winter, when rule-based approaches proved limited, leading researchers to focus on data-driven methods and neural networks. The deep learning revolution of the 2010s, powered by GPUs and large datasets, transformed computer vision and natural language processing, culminating in transformer-based models like BERT and GPT. Today, AI/ML development encompasses specialized practices—prompt engineering, vector databases, retrieval-augmented generation, and LLM observability—that treat large language models and foundation models as primary development primitives rather than experimental tools. Modern AI engineering balances capability gains against costs, alignment risks, and inference latency, reflecting a maturation from research-focused exploration to production-grade software systems.

Key concepts

Large Language Models (LLMs)
Neural Networks — Conceptual Overview
Tokenization in LLMs
Embeddings
Prompt Engineering
RAG — Retrieval-Augmented Generation
AI Hallucination
Fine-Tuning LLMs

Best references

Attention Is All You Need The foundational 2017 paper introducing the Transformer architecture, which underpins all modern LLMs. Essential reading for understanding the core mechanism behind contemporary AI models.
OpenAI API Documentation Canonical reference for working with production LLM APIs, covering models, prompt engineering, fine-tuning, and cost management patterns used across 48 terms in this category.
Anthropic's Constitutional AI Paper Seminal work on AI alignment and safety through constitutional methods. Directly relevant to AI Governance, AI Guardrails, AI Hallucination, and Constitutional AI (CAI) terms.
Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks The original 2020 RAG paper by Lewis et al., establishing the framework for combining retrieval with generation—critical context for RAG and vector database terms.
Deep Learning (Goodfellow, Bengio, Courville) Authoritative textbook covering neural networks, optimization, and foundational concepts that underpin embeddings, diffusion models, and ML types in this category.
Hugging Face Model Hub & Documentation Practical reference for model selection, fine-tuning, quantization, and tokenization with real implementations. Covers hands-on aspects of AI Model Selection, Knowledge Distillation, and model deployment.