Readit NewsFine tuning: Great if you have static data and deep pockets for compute.
RAG inclusive Vector DB: The gold standard. Think of it as having your data whisper the answers to the LLM.
With AI Squared, you can keep your data fresh, dynamic, and external because nobody wants to retrain a model every time the boss changes their mind. :D
On the AI front, vector databases like Pinecone and pgvector are exciting, but I’d love to see something even more integrated with AI workflows. The possibilities are huge. Curious to hear what others think!
Building the Multiwoven product based on Rails has been incredibly helpful in balancing rapid development with the ability to scale and customize as user demands evolve. It provides a structured yet flexible foundation, allowing us to adapt quickly without compromising on quality.
It’s about knowing when to leverage low code for speed and when to transition to more robust solutions for long-term scalability.
Real-time data allows AI systems to respond instantly to dynamic changes, improving accuracy in areas like fraud detection, predictive maintenance, and personalised recommendations.
2. Breaking Data Silos
Data activation integrates fragmented data across platforms enabling AI to access rich, unified inputs for better insights and cross functional decision making.
3. Turning Insights into Action
Reverse ETL moves AI-generated insights from data warehouses back into operational tools (like CRMs) ensuring predictions drive real-world actions.
What other ways does data activation empowers AI Models?