How to Become an Expert in ANYTHING with Deep Research

Ever needed to learn a new skill or concept fast?

I’m talking about walking into a high-stakes situation — like a big meeting or a major deadline — without spending days on Google. Recently, I had to negotiate a startup contract with zero experience, and I used OpenAI’s Deep Research to learn everything I needed in just one hour.

This article shows you exactly how I did it — and how you can, too. If you like step-by-step guides, this one’s for you.

This article is a written form of the video I made recently. If you prefer to watch instead of reading, feel free to check it out here:

The “One-Hour Expert” Approach

Why Bother?

• Save Time: Don’t waste days or weeks on scattered research.

• Gain Confidence: Walk in feeling prepared, even if you’re a total newbie.

• Make Better Decisions: Quickly get real-world wisdom from experienced people (Reddit, forums, expert blogs).

I’ve used this method to speed-learn everything from startup equity to new coding frameworks — and it works.

My Story: From Clueless to Contract-Ready

I got an invite to collaborate with a startup founder. Awesome, right? Except… he wanted to discuss contract terms (including equity, payment schedules, and more). I had never done this before, and the meeting was the next day. In the past, I might’ve panicked or spent the whole night on random Google searches.

Instead, I turned to OpenAI Deep Research. Within an hour, I gathered advice from Reddit, Stack Exchange, and top startup forums. I learned about vesting schedules, equity splits, and negotiation red flags. By the time we met, I sounded like I’d been prepping for weeks.

If you’re curious how the final plan looked like, you can review my conversation in ChatGPT.

The 5-Step Method

Below is my exact process. Feel free to adapt it to your topic — startup contracts, new software tools, marketing tactics, etc.

1. Set Clear Context

Key Idea: Give the AI context, objectives, and details.

Instead of asking something vague like What is equity? feed the AI a rich scenario— just like you’d explain to a human expert.

Remember about:

• Describing your situation

• Guiding Deep Research what sources to look at (e.g., Reddit, PubMed, etc.)

• Goals

• What output do you expect (e.g., meeting scenarios and how to respond in each situation)

Here’s the context I used:

I have a meeting with a startup founder with whom I previously talked twice. He is looking for an AI Engineer for his product.

We discussed the idea of the product (AI CPA-level US tax assistant) and we did a small exercise where I proved that I will be able to develop the solution he is looking for.

Today, we'll meet for the third time to discuss the terms of our relationship. I want to prepare for the meeting and learn how I could approach this. What are my options in terms of getting paid (or maybe I won't get paid now but only if we start making revenue), etc. I don't have previous experience working for a startup founder so I want to learn what are standards for approaching such relations (Founder-Engineer).

More context about him: He doesn't have funding yet but he's actively looking for it - he filed YC application. He wants to ship the first version of the application in a month.

I want to visualize today's meeting - how we could discuss, who should give the proposition first, how should I respond in different cases, etc.

Context about me: I work 9-5 in a company so this project will be part time but I'm willing to do the work.

Tip: The more details you give — like your role, the startup’s stage, and your goals — the better the AI can target your needs.

2. Engage with AI’s Follow-Up Questions

Deep Research always asks clarifying questions. This is your chance to:

• Refine your goals

• Spot blind spots

• Reveal any assumptions you didn’t realize you had

For instance, if the AI asks, “Are you okay with pure equity, or do you need guaranteed cash?” don’t just say “Either.” Give a real answer:

Answering these questions in detail allows AI to steer its research in the right direction, given your situation. If you want to get the best answers, make sure you provide as good of a context for the AI as possible.

3. Dive Deeper Into the Sources

Key Idea: Trust, but verify — and check the sources.

Here’s where the real magic happens. Deep Research can scour the web — Reddit threads, Q&A forums, expert blogs — and return a distilled summary with links to original sources. Instead of you spending hours sifting through countless pages, the tool does the heavy lifting.

A typical AI response looks like this:

One Reddit discussion led me directly to an incredible article by Patrick McKenzie, all about equity negotiations for early-stage founders. This one post alone had clear, actionable tips that shaped how I approached my meeting.

This makes using Deep Research feel like web browsing on steroids, quickly surfacing advice you might never find otherwise.

4. Identify Key Learnings & Patterns

After the AI delivers detailed research, you’ll have tons of information (Deep Research results are very extensive).

But to actually use it effectively, you need to identify the key takeaways — the insights that stick with you most clearly.

After you’ve gathered tons of info, distill it. That means pulling out:

• Common dos and don’ts

• Practical tips relevant to your exact situation

• Red flags you should watch out for

In my case, I ended up with a negotiation anchor:

• A 30-day paid trial period, split into two payments (half upfront, half after 30 days).

• A plan to reassess after delivering an MVP.

• Specific questions about funding if the founder couldn’t pay me until later.

Having this cheat sheet kept me calm and focused during the actual meeting.

5. Refine your insights with AI

The initial research gives you clarity, but using AI to quickly refine your notes saves time and gives you polished, ready-to-use strategies.

Here are two quick ways to take your initial findings to the next level:

• Another Round of Deep Research— Great if you still need more insights, but it gives lengthy outputs that require extra work.

• Refine directly in standard mode — Easier, faster, and tailored specifically to your already-collected notes. The AI combines your synthesis with the earlier research to improve your strategy.

Instead of just facts, request a scenario. Here’s the prompt I used for final refinement:

This is my initial anchor that I have:
My stance:
- monthly modest stipend to cover at least some of my time commitment ($2000 in total, half upfront, half at the end)
- milestone based payments:
 - integrating the smaller prompts into the platform
 - review level queries - the payment for each milestone has to be discussed
- initial 30-day agreement

- After delivering the MVP, hold a “Retro & Reassessment Meeting” to evaluate:
    - Project success
    - The founder’s updated funding status
    - Possible next steps (maintenance, further development, scaling, etc.)
- If you both want to continue, you can negotiate an **extension** or create a new agreement with revised terms.

If they don't want to pay before funding:
- "What is the plan to secure funding or generate revenue in the near term"
- "Since the funding path will impact our agreement and project timeline, could you tell me about any past experiences you've had with raising funds or working in early-stage startups"


Please review if it's reasonable and then create a scenario for negotiating the deal with the founder


The founder wants to focus on shipping the first version of the MVP in the upcoming month. This is why I'd like to only discuss the terms for the next month for now when it comes to monthly stipend and milestones.

Milestones - the milestone payments should be on top of the monthly stipend (the model would be a lower monthly stipend with milestone as performance-based payments)

Equity Considerations - I can agree on revenue sharing instead of equity

Scope of the MVP:

1. Introducing AI cues to the AI tax platform
 a) parsing IRS pdfs into rules based on the user forms
 b) integrating into the platform
2. Final AI review based on RAG
 a) preparing the data (pdf parsing) and vector store
 b) designing and developing agent-based system
 c) integration into the platform
3. Making it IRS compliant and auditable
 a) defining the IRS compliance rules
 b) aligning all of the outputs with these rules (e.g. checking for the red flags)
4. CPA-level reviews
 a) defining what does it mean
 b) designing test to check the level
 c) achieving CPA level

Funding Questions, yes, please ask about contingency plans

This final pass can make your plan more polished and ready for real life. I ended up with a script that showed me exactly how to handle different scenarios — like if the founder said, “We’re too broke to pay you now,” or if he offered an equity-only deal. It also suggested additional follow-up questions to ask.

One-Hour Expert: Why It Works

1. Focused Prompt: You’re telling the AI exactly what you need so it pulls from the right sources.

2. Clarifying Questions: Forces you to think deeper about your goals.

3. Source Verification: You’re not just trusting an AI summary; you’re reading real user stories.

4. Synthesized Notes: You end up with a short, actionable summary — perfect for quick reference.

5. Final AI Refinement: AI helps tailor your findings into a practical plan.

Conclusion

By following these five steps, you’ll avoid the typical endless Googling trap and spend just one focused hour gaining real, applicable knowledge. Whether it’s negotiating equity with a startup, diving into a new coding framework, or learning a complex topic on short notice, Deep Research can help you become a one-hour expert.

Give it a try! You might be amazed at how prepared you feel without losing days to random Google searches.

If you find this approach useful or have any tips of your own, feel free to share them in the comments. Let’s keep learning smarter, not harder!

That’s it for today! Stay tuned for the next stories soon!

Until then,

Stay curious!