AI for Productivity

Build a Personal Study Assistant

Turn a stack of lecture notes into a study partner that quizzes you, explains what you got wrong, and tracks which topics you keep failing. You will finish with a reusable system prompt you can point at any subject.

Build a Meeting Notes System

Turn rambling notes into decisions, owners and deadlines — in a format consistent enough that you can search it six months later. The value is in the structure, not the summary.

Build Your Own Prompt Library

Stop rewriting the same prompt. Build a small, organised, tested personal library — and learn the versioning habit that stops it rotting into a folder of near-duplicates.

Zero-shot prompting

Zero-shot prompting means asking a model to do a task with no worked examples — just an instruction. It works well for common tasks the model has seen extensively in training.

Reasoning model

A reasoning model is trained to generate extended internal deliberation before answering. It trades latency and cost for substantially better performance on maths, logic, coding and multi-step problems.

Open-weights model

An open-weights model has publicly downloadable parameters, letting anyone run it on their own hardware. Llama, Mistral, Qwen and DeepSeek are prominent examples.

Meeting Notes to Action Items

Turning messy notes into a shareable recap

Daily Priority Planner

Quick daily task triage

Zero-Shot vs Few-Shot Prompting — Grok

Zero-shot means asking directly with no examples.

Ask-Before-Answer for Ambiguous Tasks

For open-ended requests, instruct the model to ask clarifying questions first.

Meta-Prompting: Prompts That Write Prompts

The fastest way to a great prompt is asking the model to write and critique one.

Planner–Worker–Reviewer Teams

The most robust agent architecture is also the simplest: a Planner decomposes the goal into tasks, Workers execute one task each with narrow context, and a Reviewer checks results against acceptance criteria before anything ships.