1 Actionable AI Prompt

↓

OpenAI just released ChatGPT-5, its latest foundational model.

There are 2 improvements that caught my eye:

In practice, this means the following for finance use cases.

(PS: I mapped a full breakout of how I expect GPT-5 to impact finance here Link)

For the embedded coding capabilities, we can now do more advanced Python asks directly on the ChatGPT front end. I've noticed it recognizes to grab data directly from the 'yfinance' package meaning improved live data (price, financial statement data, etc).

As for hallucinations, we all know data fidelity has been the hardest problem to solve for reliable financial analysis using AI. ChatGPT-5 is one step closer to recognizing when it has reliable outputs vs when it is making up data.

I tested out creating a detailed comparison of Adobe ($ADBE) using SaaS specific valuation + operating metrics.

I used to take several hours doing this analysis by hand in Excel. Now we don't even need Cursor and can do this one-shot using a single prompt on the ChatGPT front end.

Here's the plan:

Ask AI to determine the comps set
Ask AI to spread multiples
Ask AI to visualize the data in pretty charts

The Prompt

Prompt (Use ChatGPT-5)

Role:

You are an expert SaaS equity analyst with deep knowledge of comparable company analysis (comps), public and private market valuations, and SaaS metric interpretation. You have access to real-time research capabilities and advanced coding features to create clean, professional data visualizations.

Objective:

I want to conduct a comprehensive comparable company analysis (comps) for Adobe ($ADBE). Make sure you also include Adobe’s own metrics in the tables and graphs. Break the task into three stages:

Stage 1 — Determine Adobe’s Comp Set

Identify a robust peer group for Adobe, including:

Direct design SaaS competitors (e.g., Figma, Sketch, Canva, Corel).

Broader creative software companies with overlapping markets (e.g., Autodesk, Unity).

Public SaaS companies with similar ARR scale, margins, and enterprise penetration.

Provide a table with:

Company Name Ticker (if public) Business Description Rationale for Inclusion Status (Public/Private) Why Relevant as a Comp

Rules:

Explain for each why it is a relevant comp (business model, pricing, growth, market served).

Use credible, up-to-date public data for business descriptions.

If the company is private, indicate “Private” in the Status column.

Stage 2 — Identify Relevant Multiples

For SaaS comps, identify the most relevant valuation multiples for Adobe’s business model. Include:

EV / Revenue (current year, forward 12 months)

EV / Gross Profit

Rule of 40 (growth rate + FCF margin)

P/E or EV/EBITDA (since Adobe is highly profitable and these are relevant for mature comps)

SaaS-specific efficiency metrics (e.g., CAC Payback, NRR) if publicly available

Output Table:

Include median, 25th percentile, and 75th percentile for each multiple at the bottom.

Clearly note “N/A” if data is unavailable.

Stage 3 — Visualize with ChatGPT-5 Python

Using ChatGPT-5’s Python coding environment, create professional, investor-grade charts:

Scatter Plot: Growth Rate vs EV/Revenue multiple (label each comp).

Bar Chart: EV/Revenue multiples sorted by size.

Bubble Chart: Revenue scale vs EV/Revenue vs Growth rate (bubble size = revenue).

Chart Requirements:

Minimalist, professional design

Labeled axes and clear titles

Investor presentation style

Exportable in PNG format

Rules & Requirements:

Use only credible public sources (e.g., SEC filings, investor decks, financial databases).

If private company data is missing, mark as “N/A” and do not estimate unless from a credible industry report.

Cite sources in footnotes under each table.

Output tables first, then charts.

The Result:

Here is the link to my ChatGPT output: Link

I was very impressed by the results. From spot checking, the data seemed fairly accurate and ChatGPT-5 fed me where it sourced the data from.

It also gave both qualitative and quantitative outputs (ie, explaining where the business overlaps with other companies + why it's included in the comps set).

We even got a few nice plots. I am playing around with more finance Python examples with the new ChatGPT-5 now that it recognizes the correct packages to use.

Quite powerful stuff! Previously this type of AI analysis required Cursor vibe coding with manual instructions on what packages to use. I'm working on mapping out for you even more use cases we can tackle together using just the new GPT-5 front end...stay tuned.

Raw excel:Adobe___SaaS_Comps___Multiples_Snapshot__Working_Table_.csv

Comps table:

Example Plots:

Posts from Me:

14 MCP servers that give Claude superpowers for finance.

I mapped 81 AI companies disrupting Wall Street.

Ever wish you had a team of analysts working 24/7?

If you made it this far, forward this to a friend who might find this helpful

Vibecoding Comps with ChatGPT-5