Prompt Engineering

How to create a proper prompt for your query is very important.

You can use this tool very easily to create proper #prompts to generate much better results for your query to #llms say #chatgpt or #bardai

Say as an example I am asking chatgpt "Details machine learning engineer job post to hire a machine learning engineer" see the response below.

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**Job Title: Machine Learning Engineer**

**Company: [Your Company Name]**

**Location: [City, State] | Employment Type: [Full-time/Contract]**

**About Us:**

[Your Company Name] is a leading [industry/domain] company dedicated to [brief company mission or values]. With a focus on innovation and technology, we are committed to transforming [industry/domain] through cutting-edge solutions. As we continue to grow, we are seeking a talented and motivated Machine Learning Engineer to join our dynamic team.

**Responsibilities:**

- Develop and implement machine learning models for [specific use case or industry application].

- Collaborate with cross-functional teams to understand business requirements and design scalable solutions.

- Work on end-to-end machine learning pipelines, including data collection, preprocessing, model training, and deployment.

- Conduct exploratory data analysis and feature engineering to enhance model performance.

- Stay updated on the latest advancements in machine learning and contribute to the company's knowledge base.

- Collaborate with data scientists and software engineers to integrate models into production systems.

**Qualifications:**

- Bachelor's/Master's/Ph.D. in Computer Science, Machine Learning, or a related field.

- Proven experience in developing and deploying machine learning models in real-world applications.

- Proficiency in programming languages such as Python, and experience with popular machine learning libraries (TensorFlow, PyTorch, scikit-learn, etc.).

- Solid understanding of machine learning algorithms, feature engineering, and model evaluation.

- Strong analytical and problem-solving skills.

- Excellent communication skills and the ability to work collaboratively in a team.

**Preferred Skills:**

- [List any additional skills or experience that would be beneficial for the role, e.g., experience with deep learning, natural language processing, etc.]

**What We Offer:**

- Competitive salary and benefits package.

- Opportunities for professional development and growth.

- [Include any additional perks, such as flexible work hours, remote work options, etc.]

**How to Apply:**

If you are passionate about machine learning and eager to contribute to innovative projects, we invite you to apply. Please send your resume, a cover letter, and any relevant work samples to [Your Email Address] with the subject line "Machine Learning Engineer Application."

**Note:**

[Include any specific details about the application process, deadlines, or additional requirements.]

[Your Company Name] is an equal opportunity employer. We value diversity and encourage candidates from all backgrounds to apply.

Join us in driving innovation and shaping the future of [industry/domain] through the power of machine learning!

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Now I am providing more details and creating a proper prompt with this FREE tool https://dhirajpatra.github.io/create_prompt/

Getting this prompt to be pasted into the chatgpt as an example.

**Question:** Details machine learning engineer job post to hire a machine learning engineer

**Main Input:**

Title: Create a JOD to hire machine learnng engineer

Content: our company is a startup with 2 years old

**Additional Inputs:**

1. Title: we have a small team

   Content: we work together as a core team for our different machine learning product

2. Title: our main product is a chatbot

   Content: one of the main product is generative ai based chatbot. which that machine learning engineer will be working with the team after selection

See the response below now from chatgpt.

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**Job Title: Machine Learning Engineer**

**Company: [Your Startup Name]**

**Location: [City, State] | Employment Type: [Full-time/Contract]**

**About Us:**

[Your Startup Name] is an innovative startup, two years into the journey, revolutionizing the landscape of [industry/domain]. With a collaborative and dynamic team, we are dedicated to pushing the boundaries of machine learning technology. We pride ourselves on being a close-knit group, working together as a core team to develop cutting-edge machine learning products.

**Current Team:**

We are a small but ambitious team, fostering a collaborative environment where each team member plays a crucial role in shaping our success. As a startup, we value the contribution of every team member and encourage an entrepreneurial spirit.

**Our Main Product:**

At the heart of our offerings is a state-of-the-art generative AI-based chatbot, a product that stands as a testament to our commitment to innovation. This chatbot represents the forefront of machine learning technology, and we are looking for a skilled Machine Learning Engineer to join our team and contribute to its further development.

**Responsibilities:**

- Collaborate with the core team to enhance and optimize our existing machine learning products.

- Contribute to the development of our generative AI-based chatbot, pushing the boundaries of conversational AI.

- Design and implement machine learning models to improve the performance and functionality of our products.

- Work closely with cross-functional teams to understand business requirements and translate them into technical solutions.

- Stay updated on the latest trends and advancements in machine learning and integrate relevant technologies into our products.

**Qualifications:**

- Bachelor's/Master's/Ph.D. in Computer Science, Machine Learning, or a related field.

- Demonstrated experience in developing and deploying machine learning models.

- Proficient in Python and experience with popular machine learning libraries (TensorFlow, PyTorch, scikit-learn, etc.).

- Strong understanding of machine learning algorithms, NLP, and conversational AI.

- Ability to work in a collaborative, startup environment with a proactive and entrepreneurial mindset.

**What We Offer:**

- A dynamic and innovative work environment where your contributions directly impact the success of the company.

- Competitive salary and equity options.

- Opportunities for professional growth and skill development.

- Flexibility and a supportive team culture.

**How to Apply:**

If you are passionate about machine learning and excited to be part of a startup journey, we invite you to apply. Please submit your resume, a cover letter, and any relevant work samples to [Your Email Address] with the subject line "Machine Learning Engineer Application - [Your Name]."

**Note:**

[Include any specific details about the application process, deadlines, or additional requirements.]

[Your Startup Name] is an equal opportunity employer. We encourage candidates from diverse backgrounds to apply and be part of our innovative journey in reshaping [industry/domain] through the power of machine learning!

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So you can see the differences above. You get more unique and correct response from LLMs when you prompt correctly. 

Prompt Engineering & Orchestration

                        Photo by Andrea Piacquadio

Prompt engineering is a buzzword, especially for Software Development.

Today we are going to learn by developing a very simple application.

We are going to assemble a set of prompts into a working prototype service, utilizing orchestration tools

to link multiple calls to AI.

Python code below

import json

import requests

# Define the AI endpoints

ai_endpoints = {

    "text_generation": "https://api.openai.com/v1/engines/davinci/completions",

    "image_generation": "https://api.openai.com/v1/images/generation"

}

# Define the orchestration tool

class Orchestrator:

    def __init__(self):

        self.ai_endpoints = ai_endpoints

    def call_ai(self, endpoint, prompt):

        headers = {

            "Authorization": "Bearer YOUR_API_KEY"

        }

        data = json.dumps({"prompt": prompt})

        response = requests.post(endpoint, headers=headers, data=data)

        return response.json()

    def assemble_service(self, prompts):

        service = {}

        for prompt, endpoint in prompts.items():

            response = self.call_ai(endpoint, prompt)

            service[prompt] = response["result"]

        return service

# Create an orchestrator object

orchestrator = Orchestrator()

# Define the prompts

prompts = {

    "text_generation": "Write a poem about a cat",

    "image_generation": "Generate an image of a cat"

}

# Assemble the service

service = orchestrator.assemble_service(prompts)

# Print the service output

print(service)

This code will call the OpenAI Text Completion and Image Generation endpoints to generate a poem and an image of a cat. The results of the AI calls are then assembled into a single service output.

This is just a simple example, of course. More complex services can be assembled by linking multiple AI calls together in a sequence or pipeline. For example, you could use the text generation output to generate an image caption, or you could use the image generation output to train a new AI model.

Orchestration tools can be used to simplify the process of linking multiple AI calls together. These tools typically provide a graphical user interface for designing and deploying workflows. Some popular orchestration tools include:

Prefect

Airflow

Kubeflow Pipelines

These tools can help you to automate the execution of your workflows and manage the dependencies between different AI calls.

Now let's create one small implementation as well.

Python code below

import json

import requests

class DCChargeManagement:

    def __init__(self, station_id):

        self.station_id = station_id

        # Define the AI endpoints

        self.ai_endpoints = {

            "predict_demand": "https://api.openai.com/v1/engines/davinci/completions",

            "optimize_charging": "https://api.openai.com/v1/images/generation"

        }

        # Define the orchestration tool

        self.orchestrator = Orchestrator()

    def predict_demand(self):

        prompt = f"Predict the demand for DC charging at station {self.station_id} in the next hour."

        response = self.orchestrator.call_ai(self.ai_endpoints["predict_demand"], prompt)

        return response["result"]

    def optimize_charging(self, demand_prediction):

        prompt = f"Optimize the charging schedule for station {self.station_id} based on the following demand prediction: {demand_prediction}"

        response = self.orchestrator.call_ai(self.ai_endpoints["optimize_charging"], prompt)

        return response["result"]

    def manage_charging(self):

        demand_prediction = self.predict_demand()

        charging_schedule = self.optimize_charging(demand_prediction)

        # Send the charging schedule to the charging station controller

        # ...

To use this class, you would first create an instance of the class, passing in the station ID as an argument. You would then call the predict_demand() method to get a prediction of the demand for DC charging at the station in the next hour. Next, you would call the optimize_charging() method to get an optimized charging schedule for the station, based on the demand prediction. Finally, you would send the charging schedule to the charging station controller.

This is just a basic example, of course. You could extend the class to include additional functionality, such as:

Support for multiple AI endpoints

Support for different orchestration tools

Support for multiple charging stations

Integration with other systems, such as a billing system or a customer relationship management (CRM) system

You could also use the class to develop a more sophisticated application, such as a mobile app that allows users to manage their DC charging sessions.