Source URL: https://simonwillison.net/2025/Apr/9/an-llm-query-understanding-service/#atom-everything
Source: Simon Willison’s Weblog
Title: An LLM Query Understanding Service
Feedly Summary: An LLM Query Understanding Service
Doug Turnbull recently wrote about how all search is structured now:
Many times, even a small open source LLM will be able to turn a search query into reasonable structure at relatively low cost.
In this follow-up tutorial he demonstrates Qwen 2-7B running in a GPU-enabled Google Kubernetes Engine container to turn user search queries like “red loveseat" into structured filters like {"item_type": "loveseat", "color": "red"}.
Here’s the prompt he uses.
Respond with a single line of JSON:
{"item_type": "sofa", "material": "wood", "color": "red"}
Omit any other information. Do not include any
other text in your response. Omit a value if the
user did not specify it. For example, if the user
said "red sofa", you would respond with:
{"item_type": "sofa", "color": "red"}
Here is the search query: blue armchair
Out of curiosity, I tried running his prompt against some other models using LLM:
gemini-1.5-flash-8b, the cheapest of the Gemini models, handled it well and cost $0.000011 – or 0.0011 cents.
llama3.2:3b worked too – that’s a very small 2GB model which I ran using Ollama.
deepseek-r1:1.5b – a tiny 1.1GB model, again via Ollama, amusingly failed by interpreting "red loveseat" as {"item_type": "sofa", "material": null, "color": "red"} after thinking very hard about the problem!
Via lobste.rs
Tags: prompt-engineering, llm, generative-ai, search, ai, llms
AI Summary and Description: Yes
Summary: The text discusses the application of a small open-source LLM (Large Language Model) for turning search queries into structured data formats. It highlights the affordability of using different models in a cloud environment, specifically within a Google Kubernetes Engine. This insight is valuable for security and compliance professionals looking at the implications and effectiveness of AI in information retrieval and data structuring.
Detailed Description:
The text elaborates on how advancements in LLM technologies are enabling more efficient data structuring from search queries, making it relevant for multiple categories like Generative AI, LLM Security, and Cloud Computing.
Key Points:
– **Query Structuring**: A small open-source LLM can effectively understand and structure search queries, presenting potential efficiencies in data handling.
– **Practical Implementation**: The service utilizes Qwen 2-7B running on Google Kubernetes Engine to convert search queries into structured JSON formats, showcasing practical AI applications.
– **Model Performance Comparison**: The text provides insights into the performance and cost-effectiveness of different AI models (Gemini 1.5 at $0.000011, llama3.2, and deepseek-r1) in processing search queries.
– **Cost Efficiency**: The reference to cost highlights the accessibility of sophisticated AI tools for even small-scale users or organizations.
Implications for Security and Compliance Professionals:
– **Data Handling Security**: Understanding how AI structures data can inform compliance with data protection regulations.
– **Model Selection**: Knowledge of model performance and costs allows for better budgeting and resource allocation within the cloud environment.
– **Innovative Processes**: These advancements may necessitate new security protocols or compliance measures as organizations increasingly adopt AI-driven solutions for data structuring and retrieval.
The discussion encapsulates the growing relevance of AI capabilities in everyday search queries and the resulting implications for cloud computing and AI security.