llm-engineering

To get structured outputs from LLMs, use the instructor library with Pydantic models for reliable data extraction. This pattern ensures type-safe responses by validating generated text against your defined schema during application development.

Manage context window limits by implementing token trimming and rolling summaries for long conversation history. This approach uses tiktoken to track token counts and condenses older messages, keeping total token counts within model limits.

Track LLM API costs and monitor observability by integrating Langfuse into your application. This setup provides detailed logging, operational cost tracking, and performance monitoring for production-ready LLM systems to identify usage bottlenecks.

Anthropic tool use supports retry logic to handle failed API calls and transient errors. Implementing retry mechanisms ensures robust execution when integrating external functions, preventing complete application failure during intermittent network issues.

Implement prompt caching to reduce repeated token processing costs and apply PII scrubbing to remove sensitive data before sending requests. These techniques optimize operational expenses and maintain data privacy in production LLM systems.

FastAPI pairs with instructor to deliver streaming responses and handle model selection routing. This architecture dynamically directs requests to appropriate models based on task complexity while serving real-time token generation to end users.