Here's the mistake most analysts make. They pick one AI tool, dump everything into it, and ask it to do the whole research job. Find the sources, read the filings, write the summary, all in one window. Then they wonder why the output is shallow, why the citations are wrong, why it hallucinated a number that was never in the document. The failure isn't the tool. It's that you used one tool for three jobs that are completely different.
So I ran a test. Same task across three tools. The task: build a research base on a company I cover, ahead of an IC memo. No coding. Just the workflow. And what I found is that Perplexity, ChatGPT, and Claude are each strong at a different stage. Use them in the right order and the whole process gets cleaner.
Stage one is source discovery. This is Perplexity. The job here is narrow. I want to know what's actually out there. Recent news, the latest 10-K, the most recent earnings call transcript, analyst commentary, regulatory filings. Perplexity is built for this because it searches live and it shows you the link for every claim. So I type something specific. "Pull the most recent quarterly earnings call transcript and the latest annual report for this company, with sources and dates." Then I check the citations. Every single one. If a source has no date, I throw it out. If it links to a summary instead of the primary document, I go find the primary document. Discovery is not synthesis. At this stage I am only collecting, and I am collecting things I can verify by clicking through. That's the whole job. Find it, date it, save the link.
Stage two is long-context reading. This is Claude. Now I have the actual documents. A two-hundred-page 10-K. A forty-page transcript. Claude handles long context well, so I paste the real filing in and I ask reading questions, not opinion questions. "Summarize the risk factors section. Quote the exact language on customer concentration. List every place management changed guidance language versus last quarter." Notice the shape of those prompts. I'm asking it to point me back to the text. Quote it. Cite the page. I am not asking what it thinks. I'm asking it to read faster than I can and show me where to look. And I still open the document and check the quote against the source. The tool narrows where I read. It does not replace the reading.
Stage three is synthesis. This is where I'll use ChatGPT. Now I've got verified sources from stage one and structured notes from stage two. The job here is to turn that into something a human can use. A comp table outline. A first draft of the bull and bear points, in my own framework. A list of the open questions I still need to answer before the memo is done. So I give it my notes, my verified facts, and I ask it to organize, not to invent. "Here are my notes and the figures I confirmed. Draft a structure for an IC memo. Flag anything where I haven't given you a source." That last line matters. I want it to tell me where the gaps are, not paper over them.
And then there's the layer that sits across all three, the part that actually keeps you out of trouble. Every claim that survives this process has four things attached. A source. A date. The assumption behind it. And a human review. That's me. Source, date, assumption, human. If any one of those is missing, the claim doesn't go in the memo. A number with no source is not a number. A source with no date is not current. An output with no human check is a draft, not a finding.
Here's why this matters. The reason single-tool research fails is that you're asking one model to discover, read, and conclude in a single step, and you lose the audit trail in the middle. You can't tell which part was sourced and which part was invented. When you split it into stages, every stage has a checkpoint. Discovery gives you links. Reading gives you quotes. Synthesis gives you structure with flagged gaps. You can trace any sentence in the final memo back to a document you actually opened.
A couple of practical notes. Don't move to stage two until stage one is clean. If your sources are weak, everything downstream is weak. Don't ask any of these tools for an opinion on the name. That's your job, and frankly it's the part of the job they can't do. And keep your prompts boring. "Quote the exact language." "List the sources." "Flag the gaps." Boring prompts give you checkable answers. Clever prompts give you confident nonsense.
So that's the workflow. Perplexity to find and date the sources. Claude to read the long documents and pull the exact language. ChatGPT to structure your verified notes into a draft. And the discipline on top: source, date, assumption, human review on every claim. Three tools, three jobs, one audit trail.
One last thing, and it's the important one. This is an educational workflow. It structures how you do research. It does not pick stocks, it does not tell you what to buy or sell, and it does not promise any outcome. The AI organizes the process. The judgment is still yours. Verify your data and do your own work before any decision. That's the whole point of keeping the human in the loop.