The prompt chaining section is the one most teams underinvest in. In production, I've found that a three-step chain (classify intent → gather context → generate response) outperforms a single carefully-crafted mega-prompt every time, even though it costs more tokens.
One pattern missing from this guide that's been huge for me: structured output constraints. Instead of asking the model to "respond in JSON," defining a strict schema (with required fields, enums for valid values, etc.) and validating the output programmatically eliminates an entire class of parsing failures. Combined with chain-of-thought in a separate reasoning step before the structured output step, you get both accuracy and reliability.
I’ve noticed that when I craft prompts using XML, the output becomes absurdly consistent.
Also I recently listened to Lenny’s podcast. The guest is Sander Schulhoff. He created the very first prompt engineering guide on the internet, two months before ChatGPT’s release. In the episode, he mentioned that it’s better to put additional information at the beginning of a prompt, for two reasons.
First, that information can get cached.
Second, if you place all the extra context at the end and it becomes very long, the model may lose track of the original task and instead latch onto a question or instruction buried in the additional information.
Solid guide! The section on Few-shot prompting is a game-changer for anyone struggling with output consistency. One thing I've noticed is that once you find a prompt that works, it's surprisingly hard to keep track of it across different projects. I've been using https://promnest.com to organize and version my templates so I don't have to reinvent the wheel every time. Thanks for sharing these techniques - proper documentation of prompts is definitely the next frontier
The prompt chaining section is the one most teams underinvest in. In production, I've found that a three-step chain (classify intent → gather context → generate response) outperforms a single carefully-crafted mega-prompt every time, even though it costs more tokens.
One pattern missing from this guide that's been huge for me: structured output constraints. Instead of asking the model to "respond in JSON," defining a strict schema (with required fields, enums for valid values, etc.) and validating the output programmatically eliminates an entire class of parsing failures. Combined with chain-of-thought in a separate reasoning step before the structured output step, you get both accuracy and reliability.
I’ve noticed that when I craft prompts using XML, the output becomes absurdly consistent.
Also I recently listened to Lenny’s podcast. The guest is Sander Schulhoff. He created the very first prompt engineering guide on the internet, two months before ChatGPT’s release. In the episode, he mentioned that it’s better to put additional information at the beginning of a prompt, for two reasons.
First, that information can get cached.
Second, if you place all the extra context at the end and it becomes very long, the model may lose track of the original task and instead latch onto a question or instruction buried in the additional information.
Solid guide! The section on Few-shot prompting is a game-changer for anyone struggling with output consistency. One thing I've noticed is that once you find a prompt that works, it's surprisingly hard to keep track of it across different projects. I've been using https://promnest.com to organize and version my templates so I don't have to reinvent the wheel every time. Thanks for sharing these techniques - proper documentation of prompts is definitely the next frontier