A naukri.com initiative
Medium
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Building AI for Privacy: Custom Recommendations with Local LLMs
- Building AI for Privacy: Custom Recommendations with Local LLMs focuses on creating a system for delivering sub-second recommendations with user privacy in mind.
- The system generates recommendations in advance to provide instant results without user wait times.
- The setup involves using a privacy-first local LLM setup based on question-response tuples and preference profiles.
- The approach aims to be cost-efficient, sustainable, and efficient by avoiding real-time processing and repetitive API requests.
- The system separates content generation and delivery, ensuring sub-second response times and scalability.
- CLI management commands in Django handle heavy lifting off-session for efficient processing.
- The workflow includes profile generation, content generation through LLM, and translation/refinement for custom recommendations.
- By using asynchronous processing, the system eliminates user wait times while maintaining privacy and quality.
- The process involves converting survey answers into structured prompts to create reliable recommendations.
- The article emphasizes building scalable, local-first AI applications that prioritize user experience and data privacy.
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