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DeepLearning.AI

Prompt Compression and Query Optimization

  • up to 1 hour
  • Intermediate

Learn how to use pre-filtering, post-filtering, and projection techniques for faster query processing and optimized query output. This course will teach you prompt compression techniques to reduce the length of prompts in large-scale applications, combining vector search capabilities with traditional database operations.

  • Pre-filtering
  • Post-filtering
  • Projection techniques
  • Prompt compression
  • Vector search

Overview

In this course, you will learn how to use pre-filtering, post-filtering, and projection techniques to optimize query processing and output. You will also explore prompt compression techniques to handle long and expensive prompts in large-scale applications. Additionally, you will learn to combine vector search capabilities with traditional database operations to build efficient and cost-effective RAG applications. This course is ideal for Python developers, database enthusiasts, and AI practitioners looking to enhance their skills in these areas.

  • Web Streamline Icon: https://streamlinehq.com
    Online
    course location
  • Layers 1 Streamline Icon: https://streamlinehq.com
    English
    course language
  • Self-paced
    course format
  • Live classes
    delivered online

Who is this course for?

Python Developers

Individuals who are familiar with Python and want to enhance their skills in query optimization and prompt compression.

Database Enthusiasts

People with a basic understanding of databases looking to learn advanced techniques for faster query processing.

AI Practitioners

Professionals interested in combining vector search capabilities with traditional database operations to build efficient RAG applications.

This course offers key benefits such as faster query processing and optimized query output through advanced techniques. It covers essential topics like pre-filtering, post-filtering, projection techniques, and prompt compression. Ideal for Python developers, database enthusiasts, and AI practitioners, this course will help you build efficient and cost-effective RAG applications.

Pre-Requisites

1 / 3

  • Familiarity with Python

  • Basic understanding of databases

  • Basic knowledge of vector search

What will you learn?

Pre-filtering Techniques
Learn how to use pre-filtering techniques to optimize query processing.
Post-filtering Techniques
Explore post-filtering techniques for enhanced query output.
Projection Techniques
Understand projection techniques to improve query efficiency.
Prompt Compression
Discover methods to reduce the length of prompts in large-scale applications.
Vector Search Capabilities
Combine vector search capabilities with traditional database operations.
Building RAG Applications
Learn to build efficient and cost-effective RAG applications.

Meet your instructor

  • Richmond Alake

    Staff Developer Advocate (AI/ML), MongoDB

    Richmond Alake is a highly experienced Staff Developer Advocate (AI/ML) with over five years of expertise in the field. He specializes in Computer Vision and Deep Learning and has a proven track record of successfully developing and integrating deep learning models to solve a wide range of problems.

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