Why Data and Context Matter More Than Prompts in AI
7 min read
·
Jul 20, 2025
In the race to adopt AI, many teams are making the same mistake: they focus almost exclusively on prompts.
The thinking goes: if we just craft the perfect prompt, the model will deliver perfect results. But this is like obsessing over baking techniques when you forgot to buy flour.
The reality is simple:
👉 Data > Context > Prompts.
The Baking Analogy: Ingredients, Recipe, Instructions
Think about building with AI the same way you think about baking a cake:
🥚 Ingredients (Data): The raw materials you have—your documents, customer interactions, transaction logs, or knowledge base.
📖 Recipe (Context): The structure, proportions, and relationships that make those ingredients work together.
🍰 Instructions (Prompts): The final steps to execute.
Without sugar or eggs, your cake will fail—no matter how carefully you fold the batter. In the same way, AI without the right data is limited.
Even with the right ingredients, if you just throw flour, eggs, and sugar into a bowl without a recipe, you may get something edible once—but you’ll never reproduce it. That’s what happens when teams throw raw data at models without structuring context.
Only when you have both ingredients and recipe does refining your instructions actually matter.
Why Teams Get Stuck on Prompts
It’s easy to see why prompts get all the attention:
They’re accessible—anyone can try tweaking them.
They feel like levers you can pull to improve results immediately.
Success stories of “prompt engineering” create the illusion that prompts are the main driver.
But focusing on prompts alone is like polishing the instructions while ignoring the missing flour. You might squeeze out marginal gains, but the model is fundamentally limited by the data and context it has to work with.
The Power of Context
A real-world example: a team spent weeks refining prompts for a workflow with little improvement. Then, a new data source became available. With that context layered in, the same prompt suddenly delivered outstanding results.
The difference wasn’t the wording of the instruction—it was the context the model had access to.
Context tells the system how the ingredients fit together:
Which data is relevant.
How different parts relate.
What patterns matter.
This is the layer that transforms scattered data into actionable intelligence.
The Hierarchy: Data → Context → Prompts
Here’s the real order of operations:
Gather ingredients (Clean Data)
Collect, clean, and structure your information. Garbage in = garbage out.
Understand the recipe (Context & Relationships)
Map how data points connect. Add the metadata, embeddings, or structures that make meaning reproducible.
Refine technique (Prompts)
Once the foundation is strong, optimize how you phrase instructions to get consistent outputs.
Most teams start at step three. That’s why their “cake” keeps collapsing.
Closing Thought
Prompts matter—but they’re the last mile, not the foundation.
If you want consistent, reliable, and high-value outputs from AI, stop obsessing over clever prompt hacks. Start with clean data. Layer in context. Then, and only then, refine your prompts.
The difference between frustration and breakthrough isn’t better wording. It’s better ingredients and a better recipe.
