Clean search results for LLM prompts using Python

This article outlines a method for cleaning search engine results before feeding them into a large language model (LLM). It emphasizes that raw API responses contain extraneous data like ads, tracking URLs, and empty fields, which can lead to noisy LLM outputs and wasted tokens. The proposed solution involves a Python script that extracts relevant information such as title, URL, and snippet, normalizes fields, cleans URLs, removes duplicates, and limits snippet length to create a concise, source-numbered context for the LLM prompt. AI

IMPACT Provides a method to improve LLM accuracy and efficiency by cleaning input data.

RANK_REASON Article describes a practical method and code for cleaning data for use with LLMs.

Read on dev.to — LLM tag →

• LLM
• Python
• SERP API

• product
• infra

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →