Basic Usage of LLM’s #loading the boiler plate code import openai import os from dotenv import load_dotenv, find_dotenv #library to load the local environment variables jupyter _=load_dotenv(find_dotenv()) api_key= os.getenv("OPENAI_API_KEY") #creating the basic prompting function client = openai.OpenAI() def get_completion(prompt, model = "gpt-3.5-turbo"): messages = [{"role": "user", "content":prompt}] response = client.chat.completions.create( model = model, messages = messages, temperature = 0 ) return response.choices[0].message.content LLM as a Transforming device Consider the below text in Sanskrit....

Basic Usage of LLM’s Loading the boiler plate code import openai import os from dotenv import load_dotenv, find_dotenv #library to load the local environment variables jupyter _=load_dotenv(find_dotenv()) api_key= os.getenv("OPENAI_API_KEY") #creating the basic prompting function client = openai.OpenAI() def get_completion(prompt, model = "gpt-3.5-turbo"): messages = [{"role": "user", "content":prompt}] response = client.chat.completions.create( model = model, messages = messages, temperature = 0 ) return response.choices[0].message.content LLM as a Inference Device Inference is essentially extraction of specific property of the text fed....

Basic Usage of LLM #loading the boiler plate code import openai import os from dotenv import load_dotenv, find_dotenv #library to load the local environment variables jupyter _=load_dotenv(find_dotenv()) api_key= os.getenv("OPENAI_API_KEY") #creating the basic prompting function client = openai.OpenAI() def get_completion(prompt, model = "gpt-3.5-turbo"): messages = [{"role": "user", "content":prompt}] response = client.chat.completions.create( model = model, messages = messages, temperature = 0 ) return response.choices[0].message.content Using ChatGPT for summarization tasks This is the most common usecase and I am sure most of the people are using ChatGPT for this activity....

Prompt Engineering as an Iterative process There is not single effective method for getting the desired results from the a LLM. For general tasks the process of getting response to a desired problem is simple, whereas for specific problems one needs to used iterative methods to arrive at desired results. In addition sometime we just do not know what the final result will look like, in such situations moulding the LLM response is essential....

Understanding Prompt Engineering We live in a knowledge economy where paramount importance must be placed on information retrieval and absorption. My journey with ChatGPT began when I started experimenting with the model and discovered use cases that were helpful to me. However, working with a general-purpose model presented its own challenges. As I continued practicing, I became acquainted with specific use cases of ChatGPT, such as efficiently performing standard NLP tasks and generating text summaries, which significantly simplified and enhanced my work....