How I Use ChatGPT’s New Project Feature to Build Domain-Specific AI Assistants
Like many professionals who operate across multiple industries and roles, I juggle contexts: construction risk management, AI research, ERP sales, personal health, and family logistics.
Each of these areas requires different expertise, tone, and memory. Instead of asking a single generalized chatbot to keep track of it all, I use ChatGPT’s New Project feature to carve out distinct spaces for each domain.
1. Treating Each Project as a Domain-Specific Expert
When I open a new project in ChatGPT, I think of it as hiring a specialized assistant. Each one has a dedicated role: • Construction Risk Manager: Supports me with risk assessment frameworks, scorecards, and case simulations. • ERP Sales Strategist: Helps me prepare client proposals, competitive positioning, and sales scripts. • AI Research Mentor: Guides my study roadmap, explains technical material, and suggests tools for building agents. • Personal Well-Being Coach: Tracks sleep, travel, and WHOOP data to keep me honest about lifestyle choices.
By isolating these domains, I avoid cross-polluting contexts. My “risk manager” doesn’t need to know about my gym schedule, and my “sales strategist” doesn’t need to read academic AI papers.
2. Building GPT Personas: Role, Voice, and Tone
Each project starts with defining a persona: • Role definition: I set a short description like “You are a seasoned construction risk officer specializing in megaproject uncertainty.” • Voice and tone: For some projects I want a concise, analytic style. For others (like my health tracker), I prefer a brutally honest “Cold War–era Russian Olympic judge” persona that keeps me accountable. • Boundaries: I clarify what the assistant should focus on—and equally important, what it should ignore.
This setup turns ChatGPT into a dedicated, specialized agent for that domain, one that “stays in character” and doesn’t drift.
3. Uploading and Updating Contextual Information
Context is what transforms a generic chatbot into a personal AI agent. Each project workspace lets me: • Upload files: I attach PDFs, spreadsheets, proposals, or lecture notes so the assistant can reference real data. For example, my “ERP Sales” agent has current product brochures and competitor analyses loaded in. • Save prompts and workflows: If I have a structured process (like a risk scoring rubric or weekly learning recap), I save it as a custom prompt in the project. This acts like a reusable “macro” the agent can run on demand. • Update continuously: Whenever I refine my thinking or produce a new artifact, I upload the latest version so the assistant stays aligned. Think of it as feeding my assistant fresh intelligence.
4. Achieving Contextual Understanding
The magic is in continuity. Within a project: • ChatGPT remembers prior conversations, files, and saved prompts. • I don’t need to re-explain background every time. • The assistant becomes progressively more context-aware, almost like an embedded team member who grows with the project.
For example: my AI Research Mentor knows which books I’ve finished, which coding tools I’m testing, and how much time I’ve allocated per week. That saves me from repetitive catch-up and lets the AI push me forward, not backward.
5. Why This Matters
We’re moving from generic Q&A toward personalized, project-anchored AI assistants. By creating multiple GPT personas—each specialized, contextual, and continuously updated—I effectively run a virtual support team. And unlike human consultants, they’re available 24/7, respond instantly, and scale with my workload.