𝗔𝗜 𝗠𝗩𝗣 𝘃𝘀 𝗣𝗼𝗖: 𝗪𝗵𝗶𝗰𝗵 𝗢𝗻𝗲 𝗦𝗵𝗼𝘂𝗹𝗱 𝗬𝗼𝘂 𝗕𝘂𝗶𝗹𝗱 𝗙𝗶𝗿𝘀𝘁?
A PoC proves the AI works. An MVP proves people want the product. Building the wrong one first wastes months.
Use this one line test. Ask yourself: Am I unsure if this can be done, or unsure if anyone wants it?
If you are unsure if it can be done, build a PoC. If you cannot answer this in 10 seconds, you do not understand your risks.
Build a PoC first if:
- You have not tested model accuracy on your actual data.
- The task involves AI weak spots like messy docs or complex reasoning.
- You do not know the cost or latency at scale.
- You need to choose between RAG, fine-tuning, or specialized models.
- A regulator or expert requires accuracy numbers before you use real users.
A PoC only needs a script and a few hundred real samples. It needs one accuracy or cost number. Do not build a UI or clean code yet.
Skip the PoC and go straight to MVP if:
- The AI capability is well established.
- The risk is how the tool fits into a workflow.
- You or a competitor already proved the capability works.
- Speed to market is your main goal.
Example: AI support ticket triage. PoC: Run 300 old tickets through different prompts. Compare results to how humans routed them. If accuracy is low, stop. You saved months of work. MVP: Connect the validated model to Zendesk. Add a simple button for agents to confirm or override the AI.
Avoid these 3 mistakes:
- PoC Creep: Do not let a messy script become your production backend. Rewrite it when you move to MVP.
- MVP Denial: Do not build the full product before checking if the model works.
- PoC Theater: Do not test on clean data. Test on messy, real-world data.
If the biggest unknown is the model, build a PoC first. Make it fast and disposable.
Source: https://dev.to/ciphernutz/ai-mvp-vs-poc-which-one-should-you-build-first-m14
Optional learning community: https://t.me/GyaanSetuAi