How can we optimize LLM-powered apps

Question

I'm building an LLM-based application (summarizer + Q&A) where inference cost is becoming significant. Using models like GPT-4 or Claude is accurate but expensive. On the other hand, switching to open-source models like LLaMA or Mistral reduces cost but drops factual precision.

Deepika Sawant · Answer

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.

How can we optimize LLM-powered apps

Answers (1)