Morgan McGuire morganmcg1

Below is a practical “cheat‑sheet” distilled from the many production prompts you just saw (Cursor, v0, Devin, Manus, Replit, Lovable, etc.). Follow these guidelines when designing prompts for your own LLM‑powered apps and you’ll get more reliable, safer and higher‑quality results.

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Nail the Three Core Elements
──────────────────────────────────────────────────────── A good prompt almost always contains ❶ role, ❷ objective, ❸ constraints / format.

• Role
“You are a senior Rust compiler engineer…”
Sets the persona, vocabulary and depth.

Great! I'll find impact shorts for skiing and snowboarding that provide strong protection, are available for purchase in Dublin stores or from EU-based online retailers, and stay within your budget of €75. I'll prioritize options with sufficient padding that meet EU protective standards and fit a women's UK size 10. I'll update you once I have the best recommendations.

Best Impact Shorts for Skiing/Snowboarding (Beginner, Under €75, Ireland/EU)

Protective impact shorts (a.k.a. crash pants) are highly recommended for beginner skiers and snowboarders. They cushion your hips, thighs, and tailbone during falls, preventing bruises especially to the coccyx (tailbone) (Impact Shorts - Buy padded impact shorts for snowboarding) ([Impact Shorts - Buy padded impact shorts for snowboarding](https://www.skatepro.ie/c493.htm#:~:text=Snowboard%20padded%20shorts%20are%20recommended,can%20benefi

	import os
	import weave
	from langchain_google_genai import ChatGoogleGenerativeAI

	os.environ["GOOGLE_API_KEY"] =

	GEMINI_MODEL_NAME = 'gemini-2.5-flash'
	CUSTOM_MODEL_NAME = 'lc_' + GEMINI_MODEL_NAME

	weave_client = weave.init("cost-testing")

	# uv pip install weave langchain-google-genai -qq

	import os
	import weave
	from langchain_google_genai import ChatGoogleGenerativeAI

	os.environ["GOOGLE_API_KEY"] =

	GEMINI_MODEL_NAME = 'gemini-2.5-flash'
	CUSTOM_MODEL_NAME = 'lc_' + GEMINI_MODEL_NAME

	[
	{
	"index": 0,
	"question": """Hey I have a question about using wandb with fastapi in a prod environment. is it recommended to initialize wandb within a specific route function, ie

	`@app.route('/')
	def my_function():
	wandb.init(...)
	`
	or should i initialize beforehand:

	ources.
	Browsing completed
	Browsing completed
	I am browsing the URL: https://llmstxt.org/
	Browsing completed
	I am browsing the URL: https://weave-docs.wandb.ai/
	Now that I have a good understanding of both the llms.txt concept and Weave's documentation, I'll create an appropriate llms.txt file for Weave. I'll focus on making it useful for LLMs to understand and work with Weave's capabilities.
	Edited the contents of a file
	I've created a comprehensive llms.txt file for Weave that follows the standard format and provides structured information about the framework. Here's what I included:
	Title (H1): Clear identification of the project

	# Weights & Biases (W&B) Weave

	> Weave is a framework for tracking, experimenting with, evaluating, deploying, and improving LLM-based applications. It provides comprehensive tools for tracing LLM calls, monitoring application behavior, systematic prompt engineering, evaluation, and deployment of guardrails in production.

	Weave is designed to support the entire lifecycle of LLM application development, from initial experimentation to production deployment. Key capabilities include:

	- Tracing and monitoring of LLM interactions and application logic
	- Systematic iteration on prompts, datasets, and models
	- Experimentation through an LLM Playground
	- Comprehensive evaluation tools with custom and pre-built scorers

	[
	{
	"index": 0,
	"question": "Hey I have a question about using wandb with fastapi in a prod environment. is it recommended to initialize wandb within a specific route function, ie\n\n `@app.route('/')\n def my_function():\n wandb.init(...)\n `\n or should i initialize beforehand:\n\n `wandb.init(...)\n @app.route('/')\n def my_function():\n ...`\n\n I'm getting a long list of log items in the console and many of them are empty.",
	"generated_answer": "We recommend initializing wandb as early as possible in your FastAPI application, preferably before defining your route functions. This helps capture any console output, including error messages, which can be useful for debugging.\nTo set up wandb in your FastAPI app:\nInitialize wandb at the start of your main application file:\nimport wandb\nwandb.init(project=\"your_project_name\")\n\napp = FastAPI()\n\[email protected]('/')\ndef my_function():\n # Your route logic here\n\nTo address the empty log items, try inc

	from weave.scorers import WeaveToxicityScorerV1

	toxicity_scorer = WeaveToxicityScorerV1()


	@weave.op
	def call_llm(prompt: str) -> str:
	"""Generate text using an LLM."""
	# Your LLM generation logic here
	return prompt.upper()

	<file_tree>
	/Users/morganmcguire/ML/marimo
	├── .github
	├── ├── ISSUE_TEMPLATE
	├── └── workflows
	├── configs
	├── dagger
	├── └── src
	├── └── └── main
	├── development_docs