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:

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