SQL Translation Bot: Few-Shot Learning, Large Language Models Explained — Prompt Engineering Course

Yann Stoneman · Beginner ·🧠 Large Language Models ·2y ago

Key Takeaways

The video demonstrates the use of few-shot learning with large language models, specifically FLAN-T5 XL, for SQL translation without expensive training or fine-tuning, using example prompting and meta learning concepts.

Full Transcript

on generative AI application where you can just use few shot prompting or example prompting instead of doing expensive training or fine-tuning is code generation you can't expect a large language model to know your database schema right and why train it that's just a couple pieces of information if we put here is the database schema for our mango shop as a prompt to the model and include some samples like prompt what are the varieties of mangoes that are sold in the shop output select variety for mangoes this is what the actual user is entering that's all they see in the chat bot for converting to SQL once that prompt gets entered you get the correct SQL translation based on your company's database and all that without fine tuning it's just one prompt with a few examples in the back end and you can start having your end users enter single prompts without knowing there's some few shot prompting going on in the background

Original Description

I used FLAN-T5 XL: https://huggingface.co/google/flan-t5-xl Amazon SageMaker Jumpstart Foundation Models: https://docs.aws.amazon.com/sagemaker/latest/dg/jumpstart-foundation-models.html Few-shot learning is an example of meta learning. Disclaimer: I work for AWS but this channel is my own and doesn’t represent AWS.
Watch on YouTube ↗ (saves to browser)
Sign in to unlock AI tutor explanation · ⚡30

This video teaches how to use few-shot learning with large language models for SQL translation, allowing users to generate SQL queries without knowing the database schema. It demonstrates the use of example prompting and meta learning concepts for generative AI applications.

Key Takeaways
  1. Define the database schema as a prompt for the large language model
  2. Include sample SQL queries as examples for the model to learn from
  3. Use few-shot learning to generate SQL queries based on user input
  4. Integrate the large language model with Amazon SageMaker Jumpstart Foundation Models
  5. Test and refine the SQL translation model
💡 Few-shot learning with large language models can be used for SQL translation, eliminating the need for expensive training or fine-tuning, and enabling users to generate SQL queries without knowing the database schema.

Related Reads

📰
DeepMind’s Hassabis says a STEM degree makes you 10x better with AI
Having a STEM degree can make you 10x better with AI, according to DeepMind's Demis Hassabis, as it provides a strong foundation for effectively using AI tools
The Next Web AI
 📰
The Math Behind Every AI Agent: A Beginner’s Guide to LLM Intelligence
Learn the mathematical foundations behind LLM intelligence and why prompting alone is not enough for optimal results
Medium · AI
 📰
The Math Behind Every AI Agent: A Beginner’s Guide to LLM Intelligence
Learn the math behind LLM intelligence and why prompting alone is not enough for AI agents
Medium · Machine Learning
 📰
I Used AI for 30 Days Instead of Google. Here’s What Actually Changed
Replace Google with AI for 30 days to discover new ways of finding information and experience the benefits and limitations of AI-powered search
Medium · ChatGPT
Up next
5 Levels of AI Agents - From Simple LLM Calls to Multi-Agent Systems
Dave Ebbelaar (LLM Eng)
Watch →