AI & ML guides, in plain English
Built for fast answers and deep understanding — what the term actually means, how it works in 2026, and exactly which roadmap to follow next.
What is RAG (Retrieval-Augmented Generation)?
Retrieval-Augmented Generation (RAG) is the dominant pattern for grounding LLMs in private or up-to-date data. Instead of storing all knowledge in the model's weights, you store it in a search index, retrieve the relevant pieces at query time, and let the LLM read them before answering.
What is an LLM Agent? (2026 Guide)
An LLM agent is an AI system that uses a language model as a reasoning engine to autonomously decide what to do next — including calling tools, querying memory, and adapting its plan. Agents differ from RAG: RAG retrieves; agents act.
Transformer Architecture Explained (2026)
The Transformer is the architecture behind GPT, Claude, Llama, Gemini, ViT, and Stable Diffusion's text encoder. It replaced recurrence with self-attention in 2017 and has stayed the dominant design ever since. Here's how it actually works.
What is Mixture of Experts (MoE)?
Mixture of Experts is the architecture that lets a 671B-parameter model run at a 37B-parameter inference cost. Instead of every token using every weight, a router sends each token to only a few specialized 'expert' sub-networks. Sparse activation, dense capacity.
LLM Interview Questions (2026)
These are the questions actually asked in LLM/AI engineer interviews at startups and FAANG in 2026. For each, we give the answer a strong candidate gives — not a textbook definition.
GenAI Interview Questions (2026)
GenAI interviews focus on shipping LLM products, not training new architectures. Expect a mix of prompting, RAG, cost/latency, safety, and 1-2 system design rounds. Here's what they actually ask.
Agentic AI Interview Questions (2026)
Agentic AI interviews assume you've shipped at least one production agent. Expect deep questions on failure modes, observability, multi-agent orchestration, and the modern protocols (MCP, A2A).
Fine-Tuning vs RAG vs Prompt Engineering
The single most common architecture question in 2026 LLM interviews and team meetings. Here's the decision framework that holds up in production.