Flooding your LLM with a massive initial prompt isn’t helping, it’s making things worse.
How many times have you dived straight into building with an LLM only to get 80% of the way there and hit a wall? That maddening loop where Claude confidently says, “I see the issue!” only to fix nothing and start again.
Context Engineering has come to prominence in the last few weeks because actually directing a model’s attention is a real challenge that requires effort and skill. The science of giving an LLM just enough context to accomplish the task at hand is a critical aspect of reliability.
It’s tempting to cram everything into your first prompt and hope for a perfect one-shot response. And while you may one-shot it once in a while, generally your model is going to fail.
The better approach is to tell your LLM, “Here’s my objective, ask me questions about it one at a time until you have enough information to move forward.”
At Bolt Foundry we use Claude Code for development, which allows us to create custom commands. In our environment we created a custom command called /questions-one-at-a-time that prompts Claude to, well, ask questions one at a time before generating code or completing a task.
At Bolt Foundry, our cardinal rule is: Discovery before developing.
This is the human version of “questions-one-at-a-time”. It forces us to pause and really understand the problem before writing a line of code (or asking Claude to do it).
Breaking a problem down into discrete questions that follow a logical flow is better for me as the user and for Claude. It helps us both focus our attention to understand the problem and create better plans.
Reliability comes from structure, not luck.
We’ve found the Hourglass method to be the best way to provide context in a structured way that doesn’t overwhelm the model.
Model attention is a scarce resource. Guard it ruthlessly and do everything in your power to focus it only on what matters most….or you’re gonna have a bad time.