Powering Local-First AI: Searching and Retrieving Context for Inference
📰 Dev.to · John Afariogun
Learn to power local-first AI by searching and retrieving context for inference, enabling more accurate and efficient AI models
Action Steps
- Build a local context store to capture and manage data for AI models
- Implement a search and retrieval system to fetch relevant context for inference
- Configure the system to prioritize context relevance and accuracy
- Test the system with various data sets to ensure robustness
- Apply the local-first AI approach to real-world applications, such as chatbots or virtual assistants
Who Needs to Know This
AI engineers and data scientists can benefit from this knowledge to improve their local-first AI applications, while product managers can use it to inform their product strategy
Key Insight
💡 Local-first AI requires efficient search and retrieval of context to enable accurate inference
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🔍 Power local-first AI with context search and retrieval! 🤖
Key Takeaways
Learn to power local-first AI by searching and retrieving context for inference, enabling more accurate and efficient AI models
Full Article
This week, I tackled the Search and Retrieval portion of our Local Context Store. If capturing data...
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