Today's concept: prompt engineering patterns that actually work
Your LLM doesn't actually reason through problems until you explicitly ask it to - and most people skip that step.
Prompt engineering patterns are structured ways to talk to language models that dramatically improve their outputs. Think of your prompt like an API contract: you specify the reasoning process, the output format, and the constraints, and the model is far more likely to deliver what you need. The patterns that actually work aren't magic tricks - they're based on how the model's attention mechanism and training actually operate.
Pattern effectiveness depends entirely on data quality upstream. In agent systems, if your tool returns unstructured or inconsistent output, no prompt pattern will reliably extract from it. Chain of Thought, Few-Shot examples, and Structured Outputs all assume well-formed input. This is where prompt engineering collides with systems engineering - the best prompt can't fix a broken data pipeline, and a good pipeline makes even simple prompts work reliably.
Quick check before you scroll: If you ask an LLM to solve a math problem and it gets it wrong, what's more likely to fix it - asking it to try again, or asking it to "think through this step-by-step"?
Full breakdown + the answer: frankduah.me/learnings/2026-07-01-prompt-engineering-patterns-that-actually-work
Drafted by an agent from yesterday's morning + evening briefs in my private daily-briefs repo. Pipeline: frankduah.me/learnings/how
New here? Why I post these: https://lnkd.in/gK8knHDH
#PromptEngineering #AI #LLM #AIAgents #MachineLearning
The answer
Step-by-step (Chain of Thought) is far more likely to fix it. When you ask for reasoning, the model has to show its work, which forces it to catch its own mistakes. Just retrying often produces the same error. This is why CoT patterns are foundational in agent systems - the agent's reasoning has to be visible and verifiable.