llm-development

# LLM & ML Development ## Frameworks - **LLM**: LangChain, transformers - **Data**: pandas, numpy - **API**: FastAPI with Pydantic ## Configuration Management - Use Hydra or YAML for experiment configs - Keep configs version-controlled - Separate dev/staging/prod configurations Example config structure: ``` config/ base.yaml models/ gpt4.yaml claude.yaml experiments/ baseline.yaml ``` ## Data Pipeline - Manage data versions with DVC - Document data sources and transformations - Use consistent data formats - Validate data at pipeline boundaries ## Model Versioning - Version models with Git LFS or model registry - Track experiments with MLflow or similar - Log hyperparameters and metrics - Save reproducibility info (seeds, versions) ## LangChain Best Practices - Use LCEL (LangChain Expression Language) for chains - Implement proper error handling for LLM calls - Add retry logic for API failures - Cache expensive operations Example: ```python from langchain_core.prompts import ChatPromptTemplate from langchain_core.output_parsers import StrOutputParser prompt = ChatPromptTemplate.from_template("Summarize: {text}") chain = prompt | llm | StrOutputParser() ``` ## Prompt Engineering - Store prompts as separate files or constants - Version control prompt templates - Test prompts with diverse inputs - Document expected outputs ## Error Handling - Catch and log LLM API errors - Implement graceful degradation - Set appropriate timeouts - Handle rate limiting ## Performance - Use async for I/O-bound LLM calls - Implement caching for repeated queries - Batch requests when possible - Monitor token usage and costs ## Testing LLM Applications - Mock LLM responses for unit tests - Create integration tests with real calls - Test edge cases and failure modes - Validate output format and structure