Chain-of-Thought (CoT) prompting asks the model to reason step-by-step before its final answer. This dramatically improves accuracy on tasks requiring logic, multi-step analysis, or decisions with real tradeoffs.
The trigger is simple: "Think step by step" — or define the reasoning steps yourself.
We're building HR tech SaaS: applicant tracking, onboarding, document management. State: 3 engineers, 40 beta customers, pre-launch.
Deciding: start with a Go monolith or go microservices from day one.
Think step by step: Step 1: Operational realities of each approach for 3 engineers with no dedicated DevOps Step 2: Specific features of HR workflows and document management that push toward one or the other Step 3: Migration cost: monolith→microservices in 18 months vs. starting microservices and finding it too complex Step 4: What do companies at exactly our stage (pre-launch, <5 engineers) typically find empirically? Step 5: Recommendation with 2 strongest reasons FOR and 1 strongest counterargument to prepare for
Don't give the recommendation before completing all 5 steps.
This Python function calculates compound interest but returns wildly wrong numbers.
def compound_interest(principal, annual_rate_pct, years):
monthly_rate = annual_rate_pct / 12
total_months = years * 12
return principal * (1 + monthly_rate) ** total_months
Test case that fails:
Work through step by step:
Claude has extended thinking on Opus models. 'Think carefully before responding' activates deeper reasoning that's visible and auditable.