Enhance your coding efficiency with querifai.ai — let AI write and debug your codes, boosting productivity, handling multiple projects effortlessly, and securing financial gains.
Overview
As a coder, sometimes it takes hours to write code and days to debug. Sifting through lines of code and dealing with stubborn bugs on a tight deadline is challenging. We've all been there. With querifai, coding becomes efficient. You can shift your focus to the bigger picture, being more productive rather than spending hours writing the codes manually and dealing with minute errors. querifai provides a coding assistant in the form of a chatbot that not only helps you write code but also helps in debugging — that's the power querifai brings to the table. However, always remember to independently verify the codes for that extra layer of reliability and accuracy.
About querifai.ai
querifai.ai is a user-friendly SaaS platform powered by AI, designed to cater to both businesses and individuals. With querifai.ai, you can easily input your data and let our system take care of the rest. Our platform harnesses the power of multiple cloud-based AI services from industry leaders like Google, AWS, and Azure, making it easy for anyone to leverage the benefits of AI technology, regardless of their level of expertise.
LLM refers to "Large Language Models" designed to understand, process, and generate human-like text based on the input it receives. The chatbot on our platform can do a variety of tasks using multiple backend service providers. However, in this article we will utilize it for coding and debugging.
How querifai Makes Your Coding Journey Easier
Efficient Coding and Debugging — Writing code manually limits the coder's ability to focus on broader project aspects. Also, dealing with persistent bugs during the debugging phase can extend the development period significantly. These bugs, often stubborn, and not easy to flag, AI assistants enhance efficiency by automating repetitive tasks, finding and debugging bugs, and simply copying the code instead of typing numerous lines manually, enabling coders to focus on more complex and creative aspects of coding.
Increased Productivity and Financial Gain — Projects that once spanned a week can now be completed in nearly half the time due to automation, allowing coders to allocate the rest of the time to multiple projects. Ultimately the coders can generate more money in less time.
A 24/7 Available Coding Assistant — Humans, unlike AI, are not available round the clock. Additionally, the traditional debugging approach involves manual searching for solutions against the error message on communities like Stackoverflow, Quora, Github. This process not only consumes valuable time but also demands a skill set for effective information retrieval.
AI assistants are available round the clock, providing assistance whenever needed, contributing to consistent workflow, and the satisfaction of being independent.
Putting the Chatbot to the Test
Now let's evaluate the performance of LLMs against varied coding challenges including writing, and debugging codes of various complexity levels ranging from beginner to complex ones.
So the next steps are:
- Create an account on querifai.ai if you haven't done so already.
- Go to https://querifai.ai/home
From the home, select the "Language" use case.
Under the Language section, click "Basic chat".
Click "New chat" to start a new conversation with the chatbot.
Set up the chat and select a language model from "Azure chatGPT 3.5", Cohere Generate, and Google Palm 2. We continuously expand our offering with more language models.
Write Codes With AI Assistant
Friendly Pair Checker
Two numbers, a and b, form a friendly pair if the sum of the proper divisors of each number equals the other number. In other words:
sum of divisors of a=b
sum of divisors of b=a
Such types of challenges are often asked in interviews or solved by beginner programmers to strengthen their logical thinking.
The following prompt is provided to the chatbot with Azure GPT 3.5 as a backend service provider.
Prompt
Write a Python code that checks if two numbers are a friendly pair or not.
Response
The API provides correct, runnable code with three test cases, and without any bugs.
You can copy the same code below and test.
def sum_divisors(n):
sum = 0
for i in range(1, n):
if n % i == 0:
sum += i
return sum
def is_friendly_pair(a, b):
sum_a = sum_divisors(a)
sum_b = sum_divisors(b)
if sum_a == b and sum_b == a:
return True
else:
return False
# Example usage
print(is_friendly_pair(220, 284)) # True
print(is_friendly_pair(1184, 1210)) # True
print(is_friendly_pair(220, 221)) # FalseSum of Boundary Elements of a Matrix
The chatbot is provided with another coding challenge this time on a 2D array in Python. This time the same prompt is provided to all three service providers. Let's find out how each service works and which one provides the most accurate results.
Prompt
Write a C program that accepts dimensions of a matrix (number of rows and columns) as input from the user and populates the matrix with random numbers between 1–10. The program should print the original matrix, boundary elements of the matrix, and the sum of all boundary elements of the matrix.
Azure GPT 3.5 Response
#include <stdio.h>
#include <stdlib.h>
#include <time.h>
int main() {
int rows, cols, i, j, sum = 0;
// Get dimensions of matrix from user
printf("Enter number of rows: ");
scanf("%d", &rows);
printf("Enter number of columns: ");
scanf("%d", &cols);
// Create matrix with random numbers
int matrix[rows][cols];
srand(time(0));
for(i = 0; i < rows; i++) {
for(j = 0; j < cols; j++) {
matrix[i][j] = rand() % 10 + 1;
}
}
// Print original matrix
printf("\nOriginal Matrix:\n");
for(i = 0; i < rows; i++) {
for(j = 0; j < cols; j++) {
printf("%d ", matrix[i][j]);
}
printf("\n");
}
// Print boundary elements of matrix
printf("\nBoundary Elements:\n");
for(i = 0; i < rows; i++) {
for(j = 0; j < cols; j++) {
if(i == 0 || i == rows-1 || j == 0 || j == cols-1) {
printf("%d ", matrix[i][j]);
sum += matrix[i][j];
} else {
printf(" ");
}
}
printf("\n");
}
// Print sum of boundary elements
printf("\nSum of Boundary Elements: %d\n", sum);
return 0;
}Testing the code
The code is tested against a test case of four rows and five columns, and the output is 100% correct and free of bugs.
Enter number of rows: 4
Enter number of columns: 5
Original Matrix:
10 8 7 3 4
10 7 8 7 1
10 3 8 3 8
7 5 9 10 7
Boundary Elements:
10 8 7 3 4
10 1
10 8
7 5 9 10 7
Sum of Boundary Elements: 99Cohere Generate Response
#include <stdio.h>
#include <stdlib.h>
#include <time.h>
#include <math.h>
int main() {
int i, j, rows, cols;
rows = cols = 10;
int arr[rows][cols];
int min = 1;
int max = 10;
int sum = 0;
srand(time(NULL));
for(i = 0; i < rows; i++)
{
for(j = 0; j < cols; j++)
{
arr[i][j] = rand() % (max - min + 1) + min;
if((i == 0 || i == rows - 1) || (j == 0 || j == cols - 1))
sum += arr[i][j];
}
}
for(i = 0; i < rows; i++)
{
for(j = 0; j < cols; j++)
{
printf("%d ", arr[i][j]);
}
printf("\n");
}
printf("\n");
printf("Boundary elements of the matrix:\n");
for(i = 0; i < rows; i++)
{
printf("%d ", arr[i][0]);
printf("%d ", arr[i][cols - 1]);
}
for(j = 0; j < cols; j++)
{
printf("%d ", arr[0][j]);
printf("%d ", arr[rows - 1][j]);
}
printf("\n");
printf("Sum of boundary elements of the matrix: %d\n", sum);
return 0;
}Testing the Code
The code successfully generates a matrix and calculates the sum for a simple test case of three rows and three columns. However, there are two identified issues:
- The code does not correctly print the boundary elements of the matrix.
- The prompt specifies that the number of rows and columns should be entered by the user, but the code uses a hardcoded value of 10 for both rows and columns.
5 7 5
6 4 2
10 10 1
Boundary elements of the matrix:
5 5 6 2 10 1 5 10 7 10 5 1
Sum of boundary elements of the matrix: 46Google Palm 2 Response
Google Palm 2 correctly generates the matrix with the desired number of rows and columns having values in the provided range (1–10). However, there are a couple of issues with the solution::
- The program asks the value of the column two times instead of one.
- The program does not print the boundary elements.
#include <stdio.h>
#include <stdlib.h>
int main() {
// Get the dimensions of the matrix from the user.
int rows, columns;
printf("Enter the number of rows: ");
scanf(" %d ", &rows);
printf("Enter the number of columns: ");
scanf(" %d ", &columns);
// Create a 2D array to store the matrix.
int matrix[rows][columns];
// Populate the matrix with random numbers between 1 and 10.
for (int i = 0; i < rows; i++) {
for (int j = 0; j < columns; j++) {
matrix[i][j] = rand() % 10 + 1;
}
}
// Print the original matrix.
printf("Original matrix: \n");
for (int i = 0; i < rows; i++) {
for (int j = 0; j < columns; j++) {
printf(" %d ", matrix[i][j]);
}
printf("\n");
}
// Find the boundary elements of the matrix.
int boundary_elements = 0;
for (int i = 0; i < rows; i++) {
for (int j = 0; j < columns; j++) {
if (i == 0 || i == rows - 1 || j == 0 || j == columns - 1) {
boundary_elements += matrix[i][j];
}
}
}
// Print the sum of boundary elements of the matrix.
printf("Sum of boundary elements: %d\n", boundary_elements);
return 0;
}Testing the Code
4
5
5
Original matrix:
2 8 5 1 10
5 9 9 3 5
6 6 2 8 2
2 6 3 8 7
Sum of boundary elements: 91The Power of Providing the Correct Prompts — Prompt Engineering
LLMs are heavily reliant on the given prompt. A well-structured prompt is the secret to achieve the desired and correct output. Just like with human Co-workers, if the LLM is provided with detailed and well-structured information, it is more likely to provide a good response.
Now, let's experiment with a slightly modified prompt to assess the performance of the LLMs that previously provided results slightly deviating from the specified requirements.
This time, we also provide sample output to the LLM, to make it absolutely clear what we are looking for as an answer.
Just like coders can solve a programming challenge easily when provided with some examples, the same is true with LLMs.
The following improved prompt is provided to Google Palm 2 once again.
Prompt
Write a C program that takes the dimensions of the matrix (number of rows and columns) from the user and populate the matrix with random numbers between 1–10. The program should print the original matrix, boundary elements of the matrix, and the sum of boundary elements of the matrix.
Sample output:
Enter number of rows: 4
Enter number of columns: 5
Original Matrix:
10 8 7 3 4
10 7 8 7 1
10 3 8 3 8
7 5 9 10 7
Boundary Elements:
10 8 7 3 4
10 1
10 8
7 5 9 10 7
Sum of Boundary Elements: 99Google Palm 2 Response
#include <stdio.h>
#include <stdlib.h>
int main() {
int rows, columns;
printf("Enter number of rows: ");
scanf(" %d ", &rows);
printf("Enter number of columns: ");
scanf(" %d ", &columns);
int matrix[rows][columns];
for (int i = 0; i < rows; i++) {
for (int j = 0; j < columns; j++) {
matrix[i][j] = rand() % 10 + 1;
}
}
printf("Original Matrix: \n");
for (int i = 0; i < rows; i++) {
for (int j = 0; j < columns; j++) {
printf(" %d ", matrix[i][j]);
}
printf("\n");
}
int sum = 0;
// Print boundary elements
printf("Boundary Elements: \n");
for (int i = 0; i < rows; i++) {
for (int j = 0; j < columns; j++) {
if (i == 0 || i == rows - 1 || j == 0 || j == columns - 1) {
printf(" %d ", matrix[i][j]);
sum += matrix[i][j];
} else {
printf(" ");
}
}
printf("\n");
}
printf("Sum of Boundary Elements: %d\n", sum);
return 0;
}Testing the Code
Surprisingly, the code is much better and almost correct now except for the part where the code still asks the number of rows two times.
We learn from this example how sensitive the answers of an LLM are towards different levels of prompt engineering. Earlier, when we did not provide the example output, Google Palm 2 got our task almost completely wrong, now it has improved significantly after being provided with an example.
Also, we can conclude that Azure GPT 3.5 performs better in a way that it provides correct output with simple prompts as well compared to the other two models that need more information.
Output
Enter number of rows: 4
5
Enter number of columns: 5
Original Matrix:
2 8 5 1 10
5 9 9 3 5
6 6 2 8 2
2 6 3 8 7
Boundary Elements:
2 8 5 1 10
5 5
6 2
2 6 3 8 7
Sum of Boundary Elements: 70Password Strength Checker
Prompt
Develop a robust password strength checker tailored for a security-conscious application with specific constraints. Ensure adherence to the following restrictions:
- Avoid relying on external password strength libraries for assessment.
- Go beyond common rules like minimum length, uppercase, and lowercase checks.
- Emphasize the minimization of false positives and negatives in the password strength assessment. Please provide a Python code snippet demonstrating the implementation of this password strength checker while addressing the specified constraints.
Azure GPT 3.5 Response
import re
def check_password_strength(password):
"""Check the strength of a password and return a score."""
score = 0
# Check for password length
if len(password) >= 8:
score += 1
# Check for lowercase letters
if re.search('[a-z]', password):
score += 1
# Check for uppercase letters
if re.search('[A-Z]', password):
score += 1
# Check for numbers
if re.search('[0–9]', password):
score += 1
# Check for special characters
if re.search('[^a-zA-Z0–9]', password):
score += 1
# Penalize common words and patterns
common_words = ['password', '123456', 'qwerty']
if password.lower() in common_words:
score -= 1
common_patterns = ['abcd', '1234', '5678']
if any(pattern in password.lower() for pattern in common_patterns):
score -= 1
# Return the password strength score
return scoreTesting the code
The code is tested for four different test cases where the program rewards one point for fulfilling the criteria of a strong password and penalizes by deducting a score of 1 when the password is weak. The output is accurate according to the code and achieved excellent results.
Sample Output
$tr0ngP@ssw0rd! Score: 5
SecurePassphrase123 Score: 4
password1234 Score: 2
123456 Score: -1Querying Firebase Realtime Database
Now let's test the LLM for slightly more complex coding challenges that involve data retrieval from Firebase real-time database into an android app written in Java. Prompt
Below is the hierarchy of my data in firebase real-time database. In my android java app, how can I retrieve all the names of hostelites whose blood group is AB+?
{
"users": {
"Hostel Manager": {
"eVqokPKIG1hnVuVlUFWI6qnzUd12": {
"blood_group": "AB+",
"name": "user1"
}
},
"Hostelites": {
"RHObGeh51OfSyvHOaKflnOqRk901": {
"blood_group": "AB+",
"name": "user3"
},
"SJOaEeh51AfVyvNMaAflnOuRd302": {
"blood_group": "A+",
"name": "user 4"
},
"TQOaQeh51OfHyvASaKflnOqWd3102": {
"blood_group": "AB+",
"name": "user2"
}
}
}
}Azure GPT 3.5 Response
The provided code snippet has undergone testing and verification, and it is correctly retrieving all the matching records.
DatabaseReference hostelitesRef = FirebaseDatabase.getInstance().getReference("users/Hostelites");
Query query = hostelitesRef.orderByChild("blood_group").equalTo("AB+");
query.addValueEventListener(new ValueEventListener() {
@Override
public void onDataChange(DataSnapshot dataSnapshot) {
for(DataSnapshot snapshot : dataSnapshot.getChildren()) {
String name = snapshot.child("name").getValue(String.class);
Log.d("Hostelite Name: ", name);
}
}
@Override
public void onCancelled(DatabaseError databaseError) {
Log.d("Error: ", databaseError.getMessage());
}
});Output
Hostelite Name: user3
Hostelite Name: user2Debug Codes With AI Assistant
A Very Basic Example in C
Let's start with a very basic example for debugging the code.
Prompt
The following C code has some errors. The comments above each part of the code shows what the code is intended to do. Please fix the bugs and provide the correct code.
Code:
include<stdio.h>
int main {
// printing a float value
float int ==5.5;
printf("\nthe value is: %d", int)
// seconds to mins conversion
int seconds=5000;
int val=0;
int mins=seconds/val;
printf("\nthere are %d minutes in %d seconds", mins);
// Percentage calculator, output should be in float
float marks_obtained= 96;
int total_marks=100;
int percentage=obtained/total*100;
printf("\npercentage: %f", percentage);
return 0;
}Azure GPT 3.5 Response
The LLM addressed and fixed all the bugs and makes the code runnable. The updated code is tested and now working correctly as expected.
Here's the corrected code:
#include <stdio.h>
int main() {
// printing a float value
float num = 5.5;
printf("\nThe value is: %f", num);
// seconds to mins conversion
int seconds = 5000;
int val = 60; // 1 minute = 60 seconds
int mins = seconds / val;
printf("\nThere are %d minutes in %d seconds", mins, seconds);
// Percentage calculator, output should be in float
float marks_obtained = 96;
float total_marks = 100; // converted to float
float percentage = marks_obtained / total_marks * 100; // converted all variables to float
printf("\nPercentage: %f", percentage);
return 0;
}Changes made:
- Added parentheses to int main.
- Changed float int to float num and fixed the format specifier in the printf statement.
- Changed int val to 60 to represent the number of seconds in a minute.
- Added seconds variable in the printf statement for the seconds to minutes conversion.
- Converted total_marks to float and changed int percentage to float percentage.
- Converted marks_obtained and total_marks to float in the calculation of percentage.
- Fixed the format specifier in the printf statement for the percentage.
Testing the Code
All the bugs have been corrected by the LLM. Below is the updated code.
The value is: 5.500000
There are 83 minutes in 5000 seconds
Percentage: 96.000000Extract Parts of a URL
The following code is intended to extract the parts of a URL. However it contains bugs so the path and domain is not extracted correctly.
Buggy Code
const url = "hello-world.org/data/abcde?hello=world&counter=5";
// Extracting the domain
const domain = url.split("/")[2]; // "hello-world.org"
// Extracting the path
const path = url.split("/").slice(3).join("/"); // "data/abcde"
// Extracting the query string
const queryString = url.split("?")[1]; // "hello=world&counter=5"
// Extracting the value of the "hello" parameter from the query string
const helloValue = queryString.split("&").find(param => param.split("=")[0] === "hello").split("=")[1]; // "world"
// Extracting the value of the "counter" parameter from the query string
const counterValue = queryString.split("&").find(param => param.split("=")[0] === "counter").split("=")[1]; // "5"
console.log("Domain:", domain);
console.log("Path:", path);
console.log("Query String:", queryString);
console.log("Hello Value:", helloValue);
console.log("Counter Value:", counterValue);Current Output
Domain: abcde?hello=world&counter=5
Path:
Query String: hello=world&counter=5
Hello Value: world
Counter Value: 5Let's utilize the LLM to fix the code. Below is a well-crafted prompt that clearly describes what the code is supposed to do, in which parts the error is arising, and what to fix. The same code is provided after writing the prompt to the LLM.
Prompt
The JavaScript code below is intended to extract different parts of a URL. However, the code contains some bugs. All other values are extracted correctly except for the "Path", and the "Domain". The correct value for the path is "data/abcde", and for the domain it is "hello-world.org". Please remove the bugs and provide the correct code.
Azure GPT 3.5 Response
const url = "hello-world.org/data/abcde?hello=world&counter=5";
// Extracting the domain
const domain = url.split("/")[0]; // "hello-world.org"
// Extracting the path
const path = url.split("/").slice(1,3).join("/"); // "data/abcde"
// Extracting the query string
const queryString = url.split("?")[1]; // "hello=world&counter=5"
// Extracting the value of the "hello" parameter from the query string
const helloValue = queryString.split("&").find(param => param.split("=")[0] === "hello").split("=")[1]; // "world"
// Extracting the value of the "counter" parameter from the query string
const counterValue = queryString.split("&").find(param => param.split("=")[0] === "counter").split("=")[1]; // "5"
console.log("Domain:", domain);
console.log("Path:", path);
console.log("Query String:", queryString);
console.log("Hello Value:", helloValue);
console.log("Counter Value:", counterValue);Testing the Code
The code now gives the correct domain. However, the path is still incorrect.
Below is the output of the updated code.
Domain: hello-world.org
Path: data/abcde?hello=world&counter=5
Query String: hello=world&counter=5
Hello Value: world
Counter Value: 5Firebase Authentication in Android Java App
Now let's test the LLM against some lengthy, and complex codes where the bug is even more hidden.
Prompt
Below is my Firebase Realtime database hierarchy.
{
"users": {
"Hostel Manager": {
"eVqokPKIG1hnVuVlUFWI6qnzUd12": {
"email": "abc@gmail.com",
"password": "abc123",
"username": "abc"
}
},
"Hostelites": {
"RHOaEeh51OfQyvKAaKflnOqWd302": {
"email": "xyz@gmail.com",
"password": "xyz123",
"username": "xyz"
}
}
}
}Why does my code say invalid username and password even though they are correct? Please find the bug.
package com.example.hall2home;
import android.content.Intent;
import android.os.Bundle;
import android.view.View;
import android.widget.Button;
import android.widget.EditText;
import android.widget.TextView;
import android.widget.Toast;
import androidx.annotation.NonNull;
import androidx.appcompat.app.AppCompatActivity;
import com.google.firebase.auth.FirebaseAuth;
import com.google.firebase.auth.FirebaseUser;
import com.google.firebase.database.DataSnapshot;
import com.google.firebase.database.DatabaseError;
import com.google.firebase.database.DatabaseReference;
import com.google.firebase.database.FirebaseDatabase;
import com.google.firebase.database.ValueEventListener;
public class MainActivity extends AppCompatActivity {
private EditText editTextUsername, editTextPassword;
private Button loginHosteliteButton, loginManagerButton;
private FirebaseAuth mAuth;
private DatabaseReference usersRef;
TextView here;
@Override
protected void onCreate(Bundle savedInstanceState) {
super.onCreate(savedInstanceState);
setContentView(R.layout.activity_main);
mAuth = FirebaseAuth.getInstance();
here = findViewById(R.id.textViewHere);
editTextUsername = findViewById(R.id.editTextUsername);
editTextPassword = findViewById(R.id.editTextPassword);
loginHosteliteButton = findViewById(R.id.loginHostelite);
loginManagerButton = findViewById(R.id.loginManager);
here.setOnClickListener(new View.OnClickListener() {
@Override
public void onClick(View view) {
Intent intent = new Intent(MainActivity.this, RegistrationOption.class);
startActivity(intent);
finish();
}
});
loginHosteliteButton.setOnClickListener(new View.OnClickListener() {
@Override
public void onClick(View v) {
loginUser("Hostlite");
}
});
loginManagerButton.setOnClickListener(new View.OnClickListener() {
@Override
public void onClick(View v) {
loginUser("Hostel Manager");
}
});
}
private void loginUser(final String userType) {
final String username = editTextUsername.getText().toString().trim();
final String password = editTextPassword.getText().toString().trim();
if (username.isEmpty() || password.isEmpty()) {
Toast.makeText(MainActivity.this, "Please enter username and password", Toast.LENGTH_SHORT).show();
return;
}
if(userType.equals("Hostelite"))
{
usersRef = FirebaseDatabase.getInstance().getReference("users").child("Hostelites");
}
else if(userType.equals("Hostel Manager"))
{
usersRef = FirebaseDatabase.getInstance().getReference("users").child("Hostel Manager");
}
// Check against Firebase Realtime Database
usersRef.addListenerForSingleValueEvent(new ValueEventListener() {
@Override
public void onDataChange(@NonNull DataSnapshot dataSnapshot) {
boolean userFound = false;
for (DataSnapshot userSnapshot : dataSnapshot.getChildren()) {
String storedUsername = userSnapshot.child("username").getValue(String.class);
String storedPassword = userSnapshot.child("password").getValue(String.class);
if (username.equals(storedUsername) && password.equals(storedPassword)) {
// Passwords match, login successful
Toast.makeText(MainActivity.this, "Login successful", Toast.LENGTH_SHORT).show();
// Navigate to the appropriate activity based on userType
Intent intent = new Intent(MainActivity.this, welcome_home.class);
startActivity(intent);
finish();
userFound = true;
break;
}
}
if (!userFound) {
Toast.makeText(MainActivity.this, "Invalid username or password", Toast.LENGTH_SHORT).show();
}
}
@Override
public void onCancelled(@NonNull DatabaseError databaseError) {
// Handle database error
Toast.makeText(MainActivity.this, "Database error", Toast.LENGTH_SHORT).show();
}
});
}
}Azure GPT 3.5 Response
Remarkably, within a lengthy and complex code, the LLM accurately identified a hidden bug and provided a solution. The error in the code was the spelling of "Hostlite" instead of "Hostelites," and as such, no corresponding node exists in the hierarchy. Many coders often spend hours in troubleshooting such issues that arises from minor spelling mistakes.
A Few Things to Consider When Using LLM as a Coding Assistant
Importance of Giving the Right Prompts
For the LLM to work well, it's essential to provide a well-crafted, specific and clear prompt. When we provide accurate questions, the LLM can focus on the important details, giving us better and more reliable results.
Enhanced Accuracy with Combined LLM Results
Code quality differs by topic and language model. It's usually worth comparing the approaches of different models towards a coding challenge, especially when the coding task is a complex one. With querifai, the underlying language model can be exchanged with a click of a button, to create exactly this comparison.
Always Use a Four-Eyes Principle
The concept involves testing and validation of codes generated by AI models through human oversight. This practice is implemented to ensure the accuracy of the AI-generated code, because AI systems often produce errors and are dependent on the quality of prompts.
Conclusions
Interesting key findings from this article prove how LLMs can transform your coding journey by saving your time and efforts, ultimately increasing your financial gain. However, findings also indicate the power of prompt reformulation to achieve improved results, particularly evident in the case of Google Palm 2. Also, we have seen that LLM works very well for basic programming challenges, however, struggles as the problem gets complex.
Coders, your time and efforts are precious so transform your coding journey through querifai.ai today. Let LLM write and debug your codes and make your coding efficient. The result? A significant boost in productivity and the ability to handle multiple projects efficiently, translating into financial gains.
Sign up now and utilize the 24/7 available LLM's as your AI coding assistants through querifai.ai.