Start With the Problem, Not the Model
The biggest mistake teams make is starting with a model instead of a problem. Before writing a single line of code, define the outcome you want to improve.
Step 1: Validate the Use Case
Ask whether the task genuinely benefits from AI. Many problems are better solved with simple rules or existing tools.
Step 2: Use Hosted Models First
Managed APIs let you prototype in hours instead of weeks. Only consider self-hosting once you understand your usage patterns and cost profile.
from openai import OpenAI
client = OpenAI()
response = client.chat.completions.create(
model="gpt-4o-mini",
messages=[{"role": "user", "content": "Summarize this article."}]
)
print(response.choices[0].message.content)
Step 3: Measure and Iterate
Instrument everything. Track quality, latency, and cost so you can make informed tradeoffs.
Conclusion
Great AI features come from disciplined product thinking, not bigger models.