How Product Actually Works
An in-product AI assistant that helps a user complete a task inside an existing workflow. A Copilot is embedded inside a host app (IDE, CRM, doc editor) and augments the user rather than replacing them. This guide pulls together everything on Onexial tagged product — 7 connected nodes across definitions, workflows, tool stacks, comparisons, prompts and applied use cases — and orders it the way you would actually learn it: vocabulary first, then process, then tooling, then execution. Every item below links to a full node with its own examples and connections, so you can go as deep as you need without losing the map.
Core concepts behind Product
Before wiring anything together, the vocabulary has to be precise. These 1 definitions cover the terms that show up in almost every Product discussion — each one links to a full entry with an example and its own connections inside the graph.
Workflows: how Product runs end to end
Concepts only matter once they become a repeatable process. Below are 3 documented workflows that apply Product to a concrete problem, with the steps, the tools involved and the variations worth testing.
Product Changelog → Marketing Assets
Every merged PR that ships a feature auto-generates a changelog entry, tweet, and email blurb.
Review Aggregation & Insight Pipeline
Turn scattered reviews into a monthly product-feedback report.
Changelog to Newsletter Automation
Turn shipped-feature notes into a monthly customer newsletter.
Prompts you can reuse
Prompts are reusable components. Each of these 3 prompts is written to be dropped into a Product workflow with minimal editing, including the context it expects and an example output.
Changelog Writer Prompt
Turn merged PRs into a user-facing changelog.
Technical Spec Writer Prompt
Convert a rough feature idea into an implementation-ready spec.
Turn an Idea Into a Build Spec (Vibe Coding)
Have an LLM convert a rough idea into a precise spec before you start building.
Frequently asked questions
- What is AI Copilot?
- A Copilot is embedded inside a host app (IDE, CRM, doc editor) and augments the user rather than replacing them. It sees the user's context, suggests actions and executes them on approval. Distinct from a standalone agent that runs autonomously.
- What is an example of AI Copilot?
- GitHub Copilot suggests code as you type. Cursor's Composer edits multiple files on request. Notion AI drafts inside the block you're in.
- Why does AI Copilot matter?
- An in-product AI assistant that helps a user complete a task inside an existing workflow.
- What should I learn after AI Copilot?
- Open the related workflows and prompts further down this page: they show how this idea is applied in a real task, step by step.
- What does the Product Changelog → Marketing Assets workflow do?
- A GitHub Action on merge to main runs an LLM chain that classifies the change and, if user-facing, generates all marketing artifacts as a PR against your marketing repo.
- What problem does Product Changelog → Marketing Assets solve?
- Product ships weekly, marketing announces monthly, and users learn about features from competitors.
- How many steps does Product Changelog → Marketing Assets take?
- 5 steps. It starts with github action triggers on merge to main. and ends with open a pr in the marketing repo with all three artifacts for human review..
- How do I set up Product Changelog → Marketing Assets step by step?
- 1) GitHub Action triggers on merge to main. 2) Classify PR: internal / bugfix / user-facing feature. Skip first two. 3) Extract the user story from the PR title, body and diff summary. 4) Generate: changelog markdown, 280-char tweet, 3-sentence email blurb. 5) Open a PR in the marketing repo with all three artifacts for human review.