Deploying LLMs in production presents unique challenges. Here's our comprehensive guide to doing it right.
Choosing Your Approach
Decide between using API-based services, self-hosting open models, or fine-tuning custom models based on your requirements for cost, latency, and customization.
Infrastructure Considerations
LLMs require significant compute resources. Consider GPU instance types, memory requirements, and whether to use spot instances for cost optimization.
Implementing RAG
Retrieval-Augmented Generation (RAG) improves accuracy by grounding responses in your data. Key components include vector databases, embedding models, and retrieval strategies.
Monitoring and Observability
Track metrics like latency, token usage, and quality scores. Implement logging that captures prompts and responses for debugging and improvement.
Cost Management
LLM costs can spiral quickly. Implement caching, prompt optimization, and usage limits to control spend.
Safety and Guardrails
Deploy content filtering, output validation, and fallback mechanisms to handle edge cases gracefully.
Continuous Improvement
Collect user feedback, track quality metrics, and continuously refine your prompts and retrieval strategies.