This AI agent has left me wondering whether traditional RAG applications have a future besides simple use cases.
PromptQL is a natural language API that executes Python and SQL-like queries on top of structured, unstructured, and any data behind an API.
The most fascinating aspect of it is how it works:
To answer a query, PromptQL creates an execution plan to access and operate the data it has access to.
The best way to understand how this works is with an example.
Imagine we write the following request: "Write an email to my latest customer describing the product they bought." The attached diagram shows every step that PromptQL will execute behind the scenes:
1. It will start by writing a SQL query to retrieve the latest customer from the database.
2. Using the customer ID, it will write a second SQL query to retrieve the list of products bought by this customer.
3. Using the list of products, it will retrieve their information from a vector store.
4. Finally, it will ask an LLM to write an email using the customer and the description of every product.
PromptQL will automatically decide where to fetch each piece of information and will use Python to orchestrate the entire query plan. If it doesn't generate a good plan, you can nudge it in the right direction by improving the initial prompt.
This is very impressive, especially compared to RAG applications, which are much less powerful and expressive than this.
There are a few other characteristics that I'm excited to see in action:
• PromptQL remembers previous interactions and uses this memory to solve complex workflows.
• It has a high tolerance for failures and can automatically self-heal and improve the data it uses.
• It can use any LLM, including your own.
Here is much more information about PromptQL:
shortclick.link/v9pbx7
Kudos to the team for all of their work and explanations. They are sponsoring this post.
It's early, but I think agentic tools like PromptQL will become the main talking point of 2025.
#رشتو
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