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llm-development

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LLM and ML development best practices with LangChain and transformers. Use when building AI/ML applications.

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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