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ChatGPT Prompt to Write a Technical Startup Pitch Deck Script

Generate a technical startup pitch deck script: slides, live demo flow, investor Q&A prep, and honest competitive moat analysis in one prompt.

Fill in the placeholders

Edit the values, then copy your finished prompt.

Your Prompt
prompt.txt
Create a pitch deck script for [company_name], a [product_description].

Stage: Seed
Asking: $2M
Current metrics: [metrics]
Team: [team_description]

Generate a 14-slide pitch deck script:

1. **Cover** — company name, tagline, visual suggestion
2. **Problem** — specific pain point with relatable story/data
3. **Solution** — your product (demo-able, not abstract)
4. **Demo Flow** — 3-minute live demo script (exactly what to show and say)
5. **Market Size** — TAM/SAM/SOM with methodology
6. **Business Model** — pricing, unit economics, LTV/CAC
7. **Traction** — metrics that matter at Seed stage
8. **Technical Architecture** — credibility slide (how it works, what is hard about it)
9. **Competitive Landscape** — 2x2 matrix positioning, honest moat analysis
10. **Team** — why this team wins (credentials + unfair advantages)
11. **Go-to-Market** — customer acquisition strategy
12. **Financials** — 3-year projections with key assumptions
13. **Ask** — specific use of funds, milestones it unlocks
14. **Closing** — memorable final statement

Also provide:
- 10 likely investor questions with prepared answers
- Red flags investors will probe and how to address them
- Timing: keep total pitch under 12 minutes

Make it a story, not a feature list. Investors fund narratives and teams, not tech.

What this prompt does

This prompt scaffolds a full 14-slide pitch deck script with hard constraints baked in: a timed demo flow, explicit market sizing methodology, a technical architecture slide built for credibility (not vanity), and an honest competitive moat analysis. The template forces you to provide real variables — your actual metrics, funding stage, and team composition — so the output stays grounded in your specific situation rather than producing generic startup language.

What makes it work structurally is the sequencing. It mirrors how experienced investors actually read decks: story first (problem, solution, demo), proof second (traction, architecture, competition), then ask. The separate investor Q&A section is generated from the same inputs, so the 10 prepared answers are calibrated to your stage and numbers — not a generic list of "hard questions."

The explicit instruction "make it a story, not a feature list" acts as a guardrail. It keeps the AI from defaulting to bullet-point product descriptions, which is the most common failure mode in AI-generated pitch content.

When to use it

  • You are preparing a seed or Series A deck and need a full narrative draft before engaging a designer.
  • Your technical co-founder needs a credibility slide that explains the architecture without dumbing it down or going too deep.
  • You are rehearsing a live pitch and need a timestamped demo script so you do not blow your time allocation.
  • You received investor feedback that your competitive positioning is weak and you need a structured 2x2 reframe.
  • You are iterating across multiple funding stages and want to quickly reframe the same core story for a different audience.

Example output

[Slide 8 — Technical Architecture]
Headline: "Hard to build. Harder to replicate."

What to say: "We process real-time inventory signals from 40+ ERP connectors
using an event-driven pipeline — Kafka ingestion, custom conflict-resolution
layer, PostgreSQL materialized views updated sub-200ms. The non-obvious
part is the conflict-resolution logic: 18 months of edge-case data from
our first three enterprise customers is baked into those rules. A new
entrant starts from zero there."

Visual suggestion: Three-column diagram — data sources → processing layer
→ customer-facing API. Highlight the middle column in brand color.

[Investor Q&A — Q3]
Q: "What stops a large ERP vendor from adding this natively?"
A: "SAP and Oracle move on 3-5 year product cycles. We already have two
customers who evaluated Oracle's roadmap and chose us because they needed
this in Q1, not 2028. Speed is the moat right now."

Pro tips

  • Fill [metrics] with specifics, not categories. "ARR $240K, MoM growth 18%, NRR 112%, 14 enterprise logos" produces a calibrated traction slide. "Good growth and retention" produces filler.
  • Set [demo_duration] shorter than you think. Investors routinely interrupt. A 4-minute demo script is safer than 8 — you can always extend, never compress gracefully.
  • The architecture slide is for a non-technical partner. If you have deeply technical moats, run this prompt once for investors and again with a more technical framing for due diligence calls.
  • Run the Q&A section separately after your first draft. Paste your generated slides back in and ask the AI to identify which answers contradict or weaken the narrative — that gap analysis is where the real prep happens.
  • Match [funding_stage] precisely to traction expectations. A pre-seed ask with Series A metrics framing creates cognitive dissonance. The prompt calibrates language per stage, but only if you are honest about where you actually are.

Frequently Asked Questions

Can I use this prompt if I don't have revenue yet?
Yes, but be precise with [metrics]. At pre-seed, metrics are things like waitlist size, pilot LOIs, weekly active users, or design partner commitments. The prompt generates a traction slide calibrated to whatever you provide — it does not invent numbers. If you leave [metrics] vague, the output will be vague.
How many slides should I actually generate?
The template supports a variable [slide_count], but 12-14 is the practical range for a 20-minute investor meeting. Going above 16 forces you to rush or skip slides mid-pitch, which signals poor preparation. If you are pitching at a demo day with 5 minutes, reduce to 8 slides and cut the financials and GTM to one combined slide.
Does the prompt produce actual financial model numbers or just slide copy?
It produces slide copy and key assumptions framing for your [projection_period], not a spreadsheet. The output will say what assumptions to state and how to present the numbers, but your actual projections need to come from a real model you built. Never let an AI invent your financial figures — investors will probe every assumption.
Engr Mejba Ahmed

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Engr Mejba Ahmed

AI Developer · Software Engineer

I'm Mejba — I design and ship production AI systems, automations, and full-stack apps. If you want this turned into a working solution for your team, let's talk.

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