Shashank Shekhar - LLMs: Beyond the Hype - A Practical Journey to Scale | PyData Global 2023

LLM application architecture typically consists of 6 key components: model selection, prompt template, vector database, LLM agents/tools, orchestrator, and monitoring module

Cost optimization strategies include:

• Using smaller models for simple queries
• LLM caching to store and reuse responses
• Quantization for model compression
• On-premise deployment for long-term savings
• Request hedging between expensive and cheaper models

RAG (Retrieval Augmented Generation) improves accuracy by:

• Retrieving relevant documents from corpus
• Chunking information into blocks
• Using vector databases for efficient storage/retrieval
• Providing external knowledge context to LLM

Evaluation and monitoring requirements:

• Offline testing before human interaction
• Monitoring latency, accuracy, throughput
• Collecting user feedback
• Measuring contextual relevance
• Tracking potential drift

Key technical considerations:

• Model licensing for commercial use
• Proper tokenizer and embedding model selection
• GPU/CPU resource optimization
• Vector database selection and setup
• Guardrails for safety and reliability

Performance improvement techniques:

• Prompt engineering and optimization
• Fine-tuning for specific tasks
• Model quantization for faster inference
• Caching frequent queries
• Using specialized models for different tasks

Security and responsible AI aspects:

• Content filtering
• Input validation
• Handling sensitive data
• Managing hallucinations
• Bias detection