Avoiding the Build Trap in AI Product Development - Part - 4/20
Product Managers should always start with the problem, not the tech.
Don't jump into building with the latest tools or trends.
First, understand the real problem you’re solving.
Avoid chasing shiny new things; focus on what truly helps your users.
Today we will see the following to avoid "Build Trap".
1. Don't start with technology, start with the problem
What it means:
Don’t build something because you have cool tech. Start with a clear problem.
In AI Product:
Don’t say “Let’s use GPT-4.” Say “Users take too long writing reports. Can AI help them summarize faster?”
Example:
Rather than building a custom computer vision model just to use TensorFlow, you realize users need help tagging images, and an off-the-shelf API does the job faster and better.
2. Avoid chasing shiny objects
What it means:
Trendy features and buzzwords can waste time. Focus on what delivers user and business value.
In AI Product:
Just because “AI agents” or “multimodal LLMs” are hot doesn’t mean your product needs them.
Example:
While others chase autonomous agents, you focus on helping users complete one workflow reliably with AI assistance—leading to better adoption and value.
3. Understand user context deeply
What it means:
How users work matters. Build features that match their real environment and workflow.
In AI Product:
Design AI tools that work with their existing tools, don’t assume users want to switch interfaces just for AI.
Example:
Instead of building a separate AI dashboard, you embed LLM-generated tips inside the CRM system agents already use.
4. Use prototypes to learn, not just to sell
What it means:
Build prototypes to test assumptions and learn what users want—not just to impress execs or investors.
In AI Product:
Quickly test a GenAI summarization tool with Lovable or Streamlit before committing to backend engineering.
Example:
You use a simple mockup with OpenAI’s API to validate that users understand and trust LLM responses, before investing 6 weeks building a UX and pipeline.
5. Create space for experimentation
What it means:
Give your team time and room to run experiments and test ideas, even if they don’t always succeed.
In AI Product:
Let data scientists test different LLM prompts or architectures without needing 100% certainty upfront.
Example:
Your team spends 1 sprint testing 3 different prompting strategies for a knowledge base assistant. One version cuts support resolution time by 30%, and that becomes the winner.
Source: LinkedIn