AI agents can be integrated into applications to understand user intents and deliver personalized responses through NLP. Facetune's implementation of a large language model (LLM)-based personal assistant simplifies their app's advanced photo editing features. Their architecture uses a Flask app on Google Cloud Run for handling

13m read timeFrom medium.com
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Table of contents
IntroductionWhat benefit do we gain from using AI Agents?Understanding Facetune’s Unique ApproachOur use case: from natural language request to an edited imageProject ArchitectureDevelopment Process1. Understanding app features and context in order to provide effective responsesMitigating LLM’s Visual “Blindspot”Embedding App-Specific Knowledge into the LLMHandling Context Window LimitationsSemantic Similarity Approach for Token Efficiency2. Ensuring Consistent Output: How to Tame the Model to Your Needs3. Handling agent limitations with a multi-agent fallback mechanismFuture DirectionsConclusion

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