How can we optimize LLM-powered apps

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  Deepika Sawant1y agoPosted Jul 8, 2025, 2:27 PM

  Fine-Tune or Instruct-Tune Open-Source Models

  If you have domain-specific data:

  • Fine-tune a smaller model (e.g., Mistral 7B or Phi-2) on your own Q&A pairs or summaries.
  • Use LoRA or QLoRA for cost-effective tuning.
  • This can close the performance gap with GPT-4 for your specific use case.

   

  Use Smaller, Specialized Models for Subtasks

  Break down tasks:

  • Use a smaller model for summarization (e.g., DistilBART or TinyLLaMA).
  • Use a more powerful model only for Q&A or reasoning.
  • Or use a rules-based summarizer (like TextRank or BERTSum) for extractive summaries.

   

  Cost-Aware Prompt Engineering

  • Use shorter, more focused prompts.
  • Avoid unnecessary context or verbose instructions.
  • Use tools like PromptLayer or LangSmith to track and optimize prompt cost vs. performance.

  Use Retrieval-Augmented Generation (RAG)

  Improve factual accuracy of open-source models by grounding them in reliable data:

  • Embed and index your documents using tools like FAISS, Weaviate, or Qdrant.
  • Retrieve relevant chunks based on the query.
  • Feed those chunks into the open-source model to answer questions or generate summaries.

  This boosts factual precision significantly without needing GPT-4 for every query.