We all know about ChatGPT. It’s profoundly expanding the possibility of creating some very smart systems. Pervasive and near-free access to LLMs (large language models) inch us closer to AGI (artificial general intelligence), which can be applied to apps and data in several ways.
Airtable, of course, is readily able to enjoy the benefits that services such as OpenAI provide to developers. Integrating the power of LLMs for text and code completion is almost trivial. These are magical capabilities, but they aren’t the only capabilities.
Most AI experts and analysts agree - AI will become pervasive in all solutions, and the ones that create the greatest customer value will blend application data, user context, and LLMs to create extremely relevant and powerful outcomes.
Rows That Have Meaning
Airtable search is not a pleasant experience at all. The findability of discrete records in a table is terrible. Locating key data across multiple tables and bases is almost impossible. I have explored this challenge with several clients, and I’m kinda thrilled to say all of that work is now obsolete. This paper needs to be burned.
Imagine if we could quickly capture the meaning of a row.
LLM embeddings make this possible. Embeddings are vectors, a fancy term for complex numeric tuples or arrays. It’s possible to get a vector for an Airtable record. The vector is a formidable representation of meaning because it is derived by associating your data with all the vectors in an LLM.
By building a simple string of key field values in a table row and using that to determine its word vectors within a model such as OpenAI’s
text-embedding-ada-002 LLM, you will know the meaning of that row. But to make this approach into a solution, you need a few more pieces of machinery; a vector datastore.
Vector databases (like Pinecone and Weaviate) have been around for a while. Still, you’ll soon hear a lot more about them because they are necessary to store the natural language essence of any information.
Opinion: If Airtable were on its game, it would already have a vector data store baked into its architecture, but sadly, I predict it will try to solve the search and findability crisis with a Lucene-like architecture that I said should now be burned.
I’m using Airtable data, vectors, and LLMs like this. It’s a bold and profoundly powerful experience when users can employ natural language to locate their own information. Or to discover related information without describing relationships through linked records.