Ensure AI model configurations and related documentation use precise terminology and provide clear, complete setup instructions without overwhelming users.
When documenting AI models, LLMs, and related infrastructure:
Use precise terminology: Distinguish between “parameters” (learned values) and “hyperparameters” (values that control the learning process). For example, demonstration examples are parameters, while max_labeled_demos and temperature are hyperparameters.
Provide complete configuration examples: Include all required parameters, environment variables, and credentials users need. For instance, when documenting model clients, specify required connection parameters and API keys:
# Good: Complete configuration
lm = dspy.Snowflake(
model="mixtral-8x7b",
credentials={
"account": "your_account",
"user": "your_username",
"password": "your_password",
"warehouse": "your_warehouse"
}
)
# Environment variables required:
# OPENAI_API_KEY="your-openai-api-key"
Start simple, then expand: Begin with essential configurations before introducing advanced options. Avoid overwhelming users with complex features upfront - start with basic functionality like llms.txt before introducing llms-full.txt.
Include performance considerations: Document recommended settings for better performance, such as async configurations and optimal hyperparameters:
dspy.settings.configure(lm=lm, async_capacity=16) # max 16 concurrent DSPy programs
dspy_model = dspy.asyncify(dspy.ChainOfThought("question -> answer"))
This approach ensures developers can successfully configure AI systems while understanding the underlying concepts and performance implications.
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