Am I the only one who spends more time debugging prompts than writing chain logic?

Okay, I have to ask... is anyone else in this boat? 🙋‍♀️ I started using LangChain to *save* time on orchestration, but lately I feel like I'm spending 80% of my effort just tweaking and debugging prompts, not building cool chains.

My chain logic is clean! But then I plug in a `ChatPromptTemplate` and the LLM goes off-script. It's a cycle of: run chain, get weird output, add another line to the system prompt to forbid that behavior, repeat. It feels like whack-a-mole.

For example, I built a simple extraction chain. The logic to format the schema and parse the output is maybe 20 lines. But getting the prompt right took forever. Here's the kind of iterative debugging I mean:

**Prompt Attempt 1 (Too vague):**
```python
system_template = "Extract the entities from the user's text."
# LLM responded with: "Here are the entities I found: ..." and then narration.
```

**Prompt Attempt 3 (Better, but not perfect):**
```python
system_template = """
Extract entities. Return ONLY a valid JSON object. Use the keys "people" and "companies".
Do not add any explanatory text.
Example: {"people": ["Alice"], "companies": ["ACME Corp"]}
"""
# LLM mostly worked, but sometimes added a trailing comma causing JSON decode errors.
```

I've started keeping a "prompt debugging" journal with failed outputs and the fixes. Best practices I'm trying to stick to now:
* **Be exhaustively explicit** about format, even stating "no markdown, no code fences."
* **Use structured output parsers (Pydantic)** wherever possible—they help a ton.
* **Include negative instructions** ("Do not...") based on past failures.
* **Write multi-shot examples** that cover edge cases I've encountered.

It's powerful when it works, but the prompt engineering feels like a separate, fragile layer. Are you all having the same experience? What's your process for stabilizing prompts? Do you just accept this as the core task now?

Happy coding!