marttp

Enhanced AI Prompt Generator

You are an AI-powered prompt generator, designed to improve and expand basic prompts into comprehensive, context-rich instructions. Your goal is to take a simple prompt and transform it into a detailed guide that helps users get the most out of their AI interactions.

Your process:

1. Understand the Input:
   • Analyze the user’s original prompt to understand their objective and desired outcome.
   • If necessary, ask clarifying questions or suggest additional details the user may need to consider (e.g., context, target audience, specific goals).

marttp

package dev.tpcoder.application.service;

import dev.tpcoder.application.dto.GetNewsByPreferenceResponse;
import dev.tpcoder.application.dto.GetNewsRequest;
import dev.tpcoder.application.dto.GetNewsResponse;
import dev.tpcoder.application.util.AgentUtil;
import org.springframework.ai.chat.client.ChatClient;
import org.springframework.ai.openai.OpenAiChatModel;
import org.springframework.beans.factory.annotation.Value;
import org.springframework.stereotype.Service;

marttp

package dev.tpcoder.application.agent;

import dev.tpcoder.application.dto.GetNewsByPreferenceRequest;
import dev.tpcoder.application.dto.GetNewsByPreferenceResponse;
import dev.tpcoder.application.dto.UserPreferenceRequest;
import dev.tpcoder.application.dto.UserPreferenceResponse;
import dev.tpcoder.application.repository.UserPreferencesRepository;
import dev.tpcoder.application.service.NewsService;
import dev.tpcoder.application.service.agent.GetNewsByUserPreferences;
import dev.tpcoder.application.service.agent.UserPreferencesService;

marttp

package dev.tpcoder.application.controller;

import dev.tpcoder.application.agent.AiAgentConfig;
import dev.tpcoder.application.dto.NewSummarized;
import dev.tpcoder.application.util.AgentUtil;
import org.slf4j.Logger;
import org.slf4j.LoggerFactory;
import org.springframework.ai.chat.messages.UserMessage;
import org.springframework.ai.chat.model.ChatResponse;
import org.springframework.ai.chat.model.Generation;

marttp

spring:
  application:
    name: spring-ai-function-calling
  ai:
    openai:
      api-key: ${OPEN_API_KEY:}

  sql:
    init:
      mode: always

marttp

INSERT INTO USER_PREFERENCES (id, category) VALUES
(1,'science'),
(2,'technology');

marttp

class Solution {

    private static final int[][] DIRS = new int[][] {
        new int[] {-1,0},
        new int[] {1,0},
        new int[] {0,-1},
        new int[] {0,1}
    };

    public int[][] updateMatrix(int[][] mat) {

marttp

class Solution {

    private static final int[][] DIRS = new int[][] {
        new int[] {-1,0},
        new int[] {1,0},
        new int[] {0,-1},
        new int[] {0,1}
    };

    public int maxAreaOfIsland(int[][] grid) {

marttp

class Solution {

    private static final int[][] DIRS = new int[][] {
        new int[] {-1,0},
        new int[] {1,0},
        new int[] {0,-1},
        new int[] {0,1}
    };

    public int numIslands(char[][] grid) {

marttp

import java.util.List;

public class Playground {

    public static void main(String[] args) {
        var nums = List.of(1, 2, 3, 15, 35, 53, 81);
        System.out.println("Element 35 exists: " + binarySearch(nums, 35));
        System.out.println("Element 40 exists: " + binarySearch(nums, 40));
    }