# LLM Configuration

# LLM Configuration

LLM configuration is where you control:

• which provider/model is used for generation
• how prompts are structured (system prompts, templates)
• safety limits (token limits, timeouts, tool restrictions)
• environment-specific policies (dev vs prod)

# Configuration goals

• Predictability: stable behavior across runs
• Safety: avoid unintended actions and data leakage
• Cost control: constrain token use and expensive operations
• Traceability: know which inputs and sources produced outputs

# Recommended configuration areas

# Provider and model selection

Choose a model based on:

• latency vs quality requirements
• context length (important for long documents)
• privacy/compliance requirements (hosted vs local/managed)

# Prompt templates

Maintain a small set of reusable templates:

• Q&A with sources
• summarization
• classification/tagging
• tool-using assistant

Templates should be versioned and promoted through environments like code.

# Tooling and endpoint access

If your assistant can call tools:

• define which endpoints/tools are available
• restrict tools based on roles and environments
• prefer endpoints that are safe and permission-aware

# Limits and guardrails

Common guardrails:

• max tokens per response
• max context size and chunking strategy
• disallow external network calls unless explicitly required

# Next steps

• Implement tool patterns safely: APIs & Extensibility → Custom Endpoints
• Structure prompts for reliability: Prompting Patterns