Most prompt engineering guides teach you to write "Act as a senior developer" and call it a day.
That works in ChatGPT. It fails in production. Th...
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This was a great insight. I never thought about using XML tags like this to structure LLM outputs.
Turning the response into something easily parseable while also forcing the model to complete the reasoning steps before the verdict is a really clever pattern.
Learned something new here.
Thanks !!
The reasoning-before-verdict ordering is the key insight there — once the model commits to structured analysis inside those XML tags, the final output is grounded in its own chain of thought rather than pattern-matching to a quick answer.
Yeah that makes sense
What clicked for me was that once you enforce structure like this, the model stops behaving like a chatbot and starts acting more like a deterministic step in a pipeline.
Exactly — that mental shift from 'chatbot' to 'pipeline step' is the key. Once you treat the model as a function with typed inputs and outputs, you can compose, test, and version prompts the same way you'd handle any other code.
That's exactly the shift — once you enforce output structure, the model becomes a reliable pipeline stage instead of a conversational wildcard. The deterministic framing is key for production because you can now write assertions against the output shape, catch regressions, and compose multiple structured calls where each step's output feeds the next predictably.
Exactly right — once you enforce structure with typed schemas and explicit reasoning steps, the model output becomes testable and predictable. That shift from chatbot to pipeline component is where production reliability starts.
That pipeline mental model is exactly right. Once you treat the LLM as a deterministic function with typed inputs and structured outputs, you can compose it with other pipeline stages — validation, routing, fallback — the same way you'd compose any other function.
@seryllns_ The key insight you picked up on is exactly right — forcing the model to complete reasoning before the verdict is not just formatting, it changes the actual output quality. When the verdict tag comes after the reasoning block, the model has to commit to a chain of logic first and then draw a conclusion from it. If you put the verdict first, the model picks an answer and then rationalizes it backward. The XML structure makes this ordering enforceable and parseable at the same time.
Structured output tags are a game-changer once you start chaining calls — forcing reasoning before the verdict basically eliminates those cases where the model jumps to a conclusion and backtracks mid-response.
The system prompt vs user message separation is something I wish more people talked about. I've seen so many codebases where everything gets shoved into one giant string and then people wonder why the model ignores half their instructions lol. The few-shot pattern is underrated too — I started doing this for structured extraction tasks and the consistency improvement was night and day compared to just describing the format.
The system prompt vs user message split is one of those things that seems obvious once you see it, but almost nobody structures their prompts that way in practice. Moving static instructions to the system prompt and keeping the user message dynamic is essentially free consistency improvement.
The few-shot observation is spot on too. For structured extraction, showing the model 2-3 examples of the exact output format eliminates most of the format drift you get with description-only prompting. It works because the model pattern-matches the examples rather than interpreting your description of the format.
@mihirkanzariya The giant string problem is real and it usually comes down to one thing: people treat the system prompt as a place to dump context rather than set behavioral constraints. The model processes system vs user messages differently during attention — system prompt instructions get higher weight in instruction-following. When you mix behavioral rules with task-specific context in one string, the model treats everything with equal weight and starts dropping instructions. For structured extraction, few-shot is almost always the right call. The model learns the schema from examples faster than from descriptions, especially for edge cases like optional fields or nested objects. One thing worth trying: negative examples alongside positive ones. Showing the model what a wrong extraction looks like often tightens consistency more than adding a third correct example.