Assisted Coding

Last updated on 2026-04-30 | Edit this page

Overview

Questions

Objectives

  • Understand the difference between traditional coding, assisted coding, and vibe coding
  • Use an LLM to create a Python script to map coordinates to counties
  • Introduce the geopandas package and geospatial concepts
  • Read through generated code to understand how it works

This lesson will use LLM-assisted coding to create a Python script that we can use to assess the coordinates in the coords.json file.

Coding styles

  • Traditional coding: Low trust. Coder writes out their code manually, referring to documentation or forums if they get stuck.
  • Assisted coding: Medium trust. Coder consults with an LLM to write blocks of code that the coder than reviews and integrates into the larger application. They can ask the LLM follow-up questions to better understand the code and test blocks as they go to ensure that code is working correctly.
  • Vibe coding: High trust, resource intensive. Coder relies on the LLM to write most/all of their code, even an entire application. They review out and provide the LLM additional prompts to modify functionality but mostly do not touch the code itself.

Using LLMs shifts the focus of the coder from writing code to reading and testing code. They can be very useful for understanding what code is doing, but be careful–some studies suggest that over-reliance on LLMs may reduce persistence and independent performance. Working through problems is critical to learning how to write and read code.

Code generated using assisted methods must be vetted before being run. Risks of running unvalidated code include:

  • Accidental deletion of files. Functions like os.unlink() or shtuil.rmtree() can delete files or entire directories. Opening a file in write mode will delete its contents.
  • Cybersquatting attacks. Generated code may include hallucinated package names, which can be used by adversarial actors to install malacious software in an attack known as slopsquatting.

Earlier in the lesson, we considered some ways we might vet coordinates returned by an LLM. Possibilities included:

  • Using a map to check each set of coordinates
  • Comparing coordinates to existing specimens with similar locality information
  • Checking whether the coordinates fall in the expected administrative division

We will work on the third option here.

Challenge

Challenge

Prompt the LLM to write Python code to determine which US county a set of coordinates is in, then answer the following questions:

  1. Can you follow the code returned by the LLM?
  2. What concepts are unfamiliar to you?
  3. How can we improve the prompt?

Remember: You can use the LLM itself to ask about unfamiliar concepts.

Concepts that commonly occur in the code returned for this prompt but that are not covered in the Python lesson include:

  • Python objects like classes, functions, and __main__
  • Geospatial concepts like shapefiles, coordinate reference systems, spatial indexes, and spatial joins
  • External libraries like geopandas, shapely, and pyogrio

Because generative AI is non-deterministic, this list is not comprehensive.

How can we improve this prompt?

Introduction to geopandas

geopandas is a geospatial library based on pandas. It allows us to draw maps and perform geospatial analyses (like calculating distances and areas) using similar syntax to pandas.

Geospatial analysis is an enormous topic. This overview will be limited to concepts that are likely to appear in the generated code.

PYTHON 

import geopandas as gpd
import pandas as pd

Let’s load the JSON file we created in the previous lesson. First we’ll use the read_json() method to load the JSON file as a DataFrame:

PYTHON 

df = pd.read_json("data/coords.json")
df

OUTPUT

country stateProvince county decimalLatitude decimalLongitude geodeticDatum coordinateUncertaintyInMeters georeferenceRemarks sourceURL
0 United States New Jersey Union 40.725962 -74.350546 WGS84 120 Locality described as 100 m southwest of the i… https://www.bing.com/maps?cp=40.726597~-74.349…

Now we’ll create a GeoDataFrame from the DataFrame:

PYTHON 

geodf = gpd.GeoDataFrame(
    df,
    geometry=gpd.points_from_xy(df["decimalLongitude"], df["decimalLatitude"]),
    crs=4326,
)
geodf

OUTPUT

country stateProvince county decimalLatitude decimalLongitude geodeticDatum coordinateUncertaintyInMeters georeferenceRemarks sourceURL geometry
0 United States New Jersey Union 40.725962 -74.350546 WGS84 120 Locality described as 100 m southwest of the i… https://www.bing.com/maps?cp=40.726597~-74.349… POINT (-74.35055 40.72596)

A coordinate reference system (CRS) is used to measure locations on or near the Earth’s surface. Components of a spatial reference include:

  • An ellipsoid that apprixmates the shape of the Earth
  • A point of origin (for example, the Prime Meridian)
  • A unit (typically either degrees or minutes)
  • Axes and order

Different CRS are suited to different tasks. Some CRS are worldwide while some are optimized for specific regions. Common CRS include:

  • WGS84 (EPSG:4326) (worldwide, used by GPS)
  • NAD83 (EPSG:XXXX) (North America)

The main thing to know here is that the CRS must be the same when comparing datasets. Changing from one coordinate system to another is referred to as projection. Use the to_crs() method to project a GeoDataFrame to another CRS. There are many ways to specify the new CRS, but the easiest is by EPSG code: "epsg:4326" or 4326:

PYTHON 

geodf = geodf.to_crs(4326)
Key Points