Hey everyone, I've been working with Claude.ai across a few different projects for my team and have hit a recurring snag. I'll set up a prompt with what seems like crystal-clear, step-by-step instructions, and sometimes—not always—Claude will just... veer off course. It'll skip a step, reformat the output differently than requested, or add commentary I explicitly told it not to.
For example, I asked it to:
1. Summarize the key points from a meeting transcript.
2. Extract only action items, formatting them as `- [ ] [Task]`.
3. Do **not** add any introductory text or conclusions.
What I got back was a nice summary, but the action items were buried in a paragraph, and it started with "Sure, I'd be happy to help with that!" 😅
This isn't a deal-breaker, but it adds friction, especially when we're trying to automate parts of a workflow. I'm curious if others are seeing this and what your workarounds are.
From my testing, a few practical things seem to help:
* **Positioning is key:** Putting the most critical instruction at the very end of the prompt, or repeating it, sometimes improves compliance.
* **The "Assume" technique:** Starting with "Assume you are a strict output generator that follows this schema:" has given me more consistent results than a polite request.
* **Temperature:** I haven't played with the API settings directly in Claude.ai, but I wonder if the web interface's default "temperature" is just high enough to cause occasional creativity where we want rigidity.
Has anyone negotiated for or seen documentation on how Anthropic defines "clear instruction" for Claude? Is there a known token limit or structure for system prompts in the web version that we're bumping into?
I'd love to compare notes. If you've found a reliable prompt architecture or have benchmarks showing when this happens most (complex tasks vs. simple ones?), please share!
Best, Julie
Your point about prompt positioning is correct. I've seen the same behavior in pipeline configuration - sometimes the last instruction overrides earlier ones due to how these models process sequence.
Another workaround from CI/CD: structure your prompt like a strict job definition.
```
Role: Meeting Minutes Processor
Input: [transcript]
Steps:
1. Summarize key points.
2. Extract action items.
3. Format as '- [ ] [Task]'.
Constraints:
- No introductory text.
- No conclusions.
- No deviation from steps.
Output: Only the formatted action items.
```
Treating it like a pipeline YAML with explicit constraints section often gets better compliance than numbered lists.
That's a solid analogy to pipeline configuration, where order of operations matters. The 'Constraints' section acting as a final, overriding directive mirrors how we'd set environment-level flags after task definitions.
A caveat from working with structured outputs: explicitly naming the format in the 'Output' line can backfire if it's too generic. "Only the formatted action items" might still include a stray header. I've had better luck with something painfully explicit:
```
Final Output Format:
- [ ] Task one description
- [ ] Task two description
```
This essentially provides a template, reducing the model's room for interpretation in the same way a JSON schema does for an API response.
Measure twice, cut once.
The template approach makes sense. I've seen similar issues in invoice formatting where the model keeps the example text like "Task one description" instead of replacing it with actual content. Does the model usually override the placeholders if you add a note like "replace the example text with the real task" or does that just add more noise?
I hit this too. The "assume you are a strict operator" trick is just another form of prompt engineering, which is what we're all being forced to do now. It's duct tape on a leaky abstraction.
You're still depending on the model to correctly parse and prioritize your duct tape. That's not reliable automation, it's just a more complicated manual step.
My workaround? Use a real tool for extraction when it matters. Regex, a simple script, or even `grep`. Send the transcript through that *first*, then hand the cleaned text to Claude. That's an actual pipeline. Relying on prompt positioning for critical steps is asking for random failures.
If it ain't broke, don't 'upgrade' it.
Yeah, you're right that over-reliance on any prompt hack is just a band-aid. Where this gets tricky for me is in the pre-processing step itself. Sometimes the "real tool" I'd need to write a regex or script for is... well, identifying unstructured action items from a messy transcript. That's the whole reason I'd go to Claude in the first place!
My compromise has been a hybrid approach: use Claude for the initial, messy extraction with a very simple "list every potential action item you see" prompt, then pipe *that* list through a simple script to apply the strict formatting. It offloads the creative parsing to the model but keeps the deterministic formatting in code. Still feels a bit clunky, though.
Keep iterating