Practical Problem Definition for AI Projects: A Developer-First Guide
This article emphasizes the importance of defining the problem statement correctly before starting an AI project. It provides a step-by-step guide for developers to identify the real business pain, define the output contract, and choose the right tools to solve the problem effectively.
Why it matters
Properly defining the problem statement is critical for the success of any AI project, as it ensures the solution is aligned with the actual business needs.
Key Points
- 1A good AI problem definition gives developers clarity on what the system will do, what 'good' and 'unsafe' looks like, and how to decide go/no-go without politics.
- 2Start by writing the current 'as-is' workflow to identify the actual bottleneck, before automating the wrong step.
- 3Define the output contract for the AI system, including data types, metadata, error modes, and human review requirements.
- 4Evaluate if a non-AI solution like a rules engine, search index, or simple automation can solve 80% of the pain before jumping to AI.
- 5Choose the right tool class (conventional software, traditional ML, or large language models) based on the task type before selecting a specific model.
Details
The article emphasizes that many AI projects fail not due to technical issues with the code or models, but because the initial problem statement was not defined correctly. The author suggests starting with a measurable description of the business pain, rather than just saying 'we need an AI solution'. This allows developers to have a clear understanding of the system requirements, acceptance criteria, and decision-making process. The key steps outlined include: 1) Documenting the current 'as-is' workflow to identify the actual bottleneck, 2) Defining the output contract for the AI system upfront, 3) Evaluating if a non-AI solution can solve most of the problem, and 4) Choosing the right tool class (software, ML, or language models) based on the task type. The author cautions against jumping straight to 'using AI' without properly scoping the problem first, as this often leads to fitting AI into whatever pain is nearby, rather than solving the core issue.
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