Generic selectors
Exact matches only
Search in title
Search in content
Post Type Selectors
Filter by Categories
About Article
Analyze Data
Archive
Best Practices
Better Outputs
Blog
Code Optimization
Code Quality
Command Line
Daily tips
Dashboard
Data Analysis & Manipulation
Data Engineer
Data Visualization
DataFrame
Delta Lake
DevOps
DuckDB
Environment Management
Feature Engineer
Git
Jupyter Notebook
LLM
LLM
Machine Learning
Machine Learning
Machine Learning & AI
Manage Data
MLOps
Natural Language Processing
NumPy
Pandas
Polars
PySpark
Python Tips
Python Utilities
Python Utilities
Scrape Data
SQL
Testing
Time Series
Tools
Visualization
Visualization & Reporting
Workflow & Automation
Workflow Automation

Maximize Accuracy and Relevance with External Data and LLMs

Table of Contents

Maximize Accuracy and Relevance with External Data and LLMs

Combining external data and an LLM offers the best of both worlds: accuracy and relevance. External data provides up-to-date information, while an LLM can generate text based on input prompts. Together, they enable a system to respond helpfully to a wider range of queries.

Mirascope simplifies this combination with Pythonic code. In the example above, we use an LLM to process natural language prompts and query the database for data.

Link to Mirascope.

Leave a Comment

Your email address will not be published. Required fields are marked *

0
    0
    Your Cart
    Your cart is empty
    Scroll to Top

    Work with Khuyen Tran

    Work with Khuyen Tran