Prompt Context

Understanding how LLMs remember your chat conversation and how it provides prompt context

Prompt Context is the concept of your prompt inferring data, mostly typically used on the target of your prompt, from previous prompts and responses.

You can use prompt context to tell a model like ChatGPT for do something to the last response, or a previous message that is identified by a specifier.

Let’s walk through a complex example.

• Prompt: “create a simple for loop in javascript”

• Response: {Simple For Loop Script}

• Prompt: “update the sample above to iterate through a list of bicycle brands”

• Response: {Updated for loop script with bicycle brand names}

• Prompt: “create a simple http request to get data”

• Response: {Simple HTTP Request Script}

• Prompt: “update the http request to return the data from the for loop response above and merge the for loop into the script”

• Response: {Integrated script with HTTP request and for loop with car data}

The “above” keyword in this example instructs ChatGPT to reference data from the conversation context.

“Above” is just one contextual word. You can use words or phrases like before ,last response , or last msg to specify a target from the conversation.

Handling Large Prompt Contexts

As you delve deeper into generative AI, you may encounter situations where the amount of text you need to feed into the language model exceeds its limits. To overcome this challenge, you can create an automated process to iterate through the context and break it into smaller, manageable chunks. This article will provide examples of how to load large amounts of data using the Express framework and Node.js.

Example 1: Loading data from a file and sending it in chunks

Suppose you have a large text file containing the data you need to process using a generative AI model. You can use the following Express server to read the file and send the data in smaller chunks:

const express = require("express");
const fs = require("fs");
const readline = require("readline");

const app = express();
const port = 3000;

app.get("/process-data", async (req, res) => {
  const fileStream = fs.createReadStream("large_data_file.txt");
  const rl = readline.createInterface({ input: fileStream, crlfDelay: Infinity });

  let dataChunk = "";

  for await (const line of rl) {
    dataChunk += line;

    if (dataChunk.length >= 2048) {
      // Process dataChunk with generative AI model
      console.log("Processing data chunk:", dataChunk);

      // Reset dataChunk for the next iteration
      dataChunk = "";
    }
  }

  if (dataChunk.length > 0) {
    // Process remaining dataChunk with generative AI model
    console.log("Processing remaining data chunk:", dataChunk);
  }

  res.send("Data processing completed.");
});

app.listen(port, () => {
  console.log(`Server running at http://localhost:${port}`);
});

Example 2: Paginating API data

In another scenario, you may need to load large amounts of data from an API. You can paginate the API data and load it in smaller chunks using the following Express server:

const express = require("express");
const axios = require("axios");

const app = express();
const port = 3000;

app.get("/process-api-data", async (req, res) => {
  const apiUrl = "https://api.example.com/data";
  let currentPage = 1;
  let hasMoreData = true;

  while (hasMoreData) {
    try {
      const response = await axios.get(apiUrl, { params: { page: currentPage, per_page: 50 } });
      const data = response.data;

      if (data && data.length > 0) {
        // Process data with generative AI model
        console.log("Processing data:", data);

        // Move on to the next page
        currentPage++;
      } else {
        hasMoreData = false;
      }
    } catch (error) {
      console.error("Error fetching data from API:", error);
      break;
    }
  }

  res.send("API data processing completed.");
});

app.listen(port, () => {
  console.log(`Server running at http://localhost:${port}`);
});

These examples demonstrate how to handle large prompt contexts by breaking the data into smaller chunks and processing them individually. By implementing such solutions, you can effectively work with large datasets and overcome the limitations imposed by generative AI models.