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:

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