Guides & definitions

Question-led explainers built for answer engines and practitioners—short definitions, clear sections, and links to the courses that teach each topic.

What is retrieval-augmented generation (RAG)?

Retrieval-augmented generation (RAG) is an architecture that retrieves relevant documents at query time and conditions a language model on that context, so answers stay grounded in up-to-date or proprietary knowledge without full model retraining.

What are LoRA and QLoRA?

LoRA adapts large language models by training low-rank matrices beside frozen weights. QLoRA combines 4-bit quantization with LoRA so teams can fine-tune capable models on limited GPU memory.

What is AWS Bedrock?

Amazon Bedrock is a fully managed service that exposes foundation models from Amazon and third parties through a unified API, with AWS-native security, networking, and tooling for building GenAI applications.

What is data engineering on AWS?

Data engineering on AWS is the practice of ingesting, transforming, storing, and serving data with services such as S3, Glue, EMR, Redshift, Kinesis, and Lake Formation so analytics and ML workloads have reliable, governed datasets.

How do you approach system design for ML interviews?

ML system design interviews test whether you can turn ambiguous product goals into a reliable machine learning system—covering data, training, serving, evaluation, and operations under explicit constraints.

How should executives think about GenAI ROI?

GenAI ROI is the measurable value of generative AI initiatives after model costs, engineering effort, risk controls, and change management—not the wow-factor of a chatbot demo.

Why do AI developers learn CUDA?

CUDA is NVIDIA’s parallel computing platform for writing programs that run on GPUs. AI developers learn it to understand performance, memory, and custom kernels when frameworks alone leave performance on the table.