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Optimization Recipes

Use LangSmith to help optimize your LLM systems, so they can continuously learn and improve.

  • Prompt Bootstrapping: Optimize your prompt over a set of examples by incorporating human feedback and an LLM prompt optimizer. Works by rewriting an optimized system prompt.
    • Prompt Bootstrapping for style transfer: Elvis-Bot: Extend prompt bootstrapping to generate outputs in the style of a specific persona. This notebook demonstrates how to create an "Elvis-bot" that mimics the tweet style of @omarsar0 by iteratively refining a prompt using Claude's exceptional prompt engineering capabilities and feedback collected through LangSmith's annotation queue.
  • Iterative Prompt Optimization: Streamlit app demonstrating real-time prompt optimization based on user feedback and dialog, leveraging few-shot learning and a separate "optimizer" model to dynamically improve a tweet-generating system.
  • Automated Few-shot Prompt Bootstrapping: Automatically curate the most informative few-shot examples based on performance metrics, removing the need for manual example engineering. Applied to an entailment task on the SCONE dataset.

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