Want to Develop a ChatGPT App? Start Here.

In the past weeks, I have built GPT-powered apps leveraging OpenAI 's API, such as ChatGPT-3.5-Turbo, to automate otherwise long and manual processes and design smart chat assistants. In this article, I want to share some of my learnings, specifically covering Python and other technical skills needed to develop and deploy your own ChatGPT tool.

My goal is to empower other aspiring developers and Python users to take advantage of the latest innovation that has shaken the world of AI and LLMs and spark conversation around the tools and skills needed for success in this field.

1. Getting Started: The OpenAI API

The OpenAI API, in particular, offers developers a simple and efficient way to integrate the latest GPT models into their applications. To interact with GPT-powered models, start by creating an account here, and generate your own secret API Key. OpenAI will give you a solid amount of credits to play around with before asking for your credit card. Start by familiarizing yourself with the API documentation and key concepts, such as authentication, endpoint usage, and rate limits. In the API section of the OpenAI website, you will also have access to their prompt engineering playground.

2. Prompt Engineering

Designing effective prompts is crucial for obtaining high-quality results from GPT models. OpenAI's playground is an excellent tool for experimenting with different prompts and API parameters. Here are some key areas to focus on:

Understand the difference between different prompt roles:

1. User prompts: You are likely already familiar with these, as these are the instructions that you usually type in ChatGPT's UI when using the online chat. User prompts are the input queries or statements provided by the end-user or developer to initiate a conversation or request specific information from the AI model. They set the context and the intent for the AI to respond accordingly.
2. System prompts: These are instructions, context, or guiding information provided to the AI model by the developer or system to better control and direct the model's behavior. They can be used to set the tone, provide additional context, or instruct the AI to perform specific tasks or follow predetermined rules when responding to the user prompt.
3. Assistant prompts: These are the AI-generated responses or pieces of information that the AI model provides in response to user or system prompts. They showcase the model's understanding of the input and its ability to generate relevant, accurate, and coherent responses while adhering to the constraints or guidance provided.

Here are some example prompts for each prompt role:

1. System prompt: The assistant is helpful at writing articles for LinkedIn. It is specialized in the domain of AI and LLM, specifically GPT. Assistant writes in a professional and technical tone, without exceeding 1000 words per article.
2. User prompt: Write an article for aspiring Python coders learning how to build apps leveraging OpenAI's ChatGPT API.
3. Assistant prompt: Here is an Article on...

Now, let's focus on learning about how different prompt parameters will affect your output:

1. max_tokens: The maximum number of tokens (words and punctuation) that you want the response to be. Setting an appropriate value for max_tokens can help ensure that the output is neither too short nor excessively long, and is informative and coherent.
2. temperature: Affects the randomness and creativity of the generated text. A higher value (e.g., 1.0) will result in more diverse and creative responses, while a lower value (e.g., 0.1) will make the output more deterministic and focused. Adjusting the temperature allows you to balance the trade-off between creativity and consistency.
3. top_p: An alternative to temperature, top_p controls the nucleus sampling method used in response generation. It's a value between 0 and 1 that represents the cumulative probability of selecting the most likely tokens. A higher value (e.g., 0.9) includes a broader set of tokens, resulting in more diverse outputs, while a lower value (e.g., 0.5) narrows the selection and produces more focused responses.
4. n: Determines the number of responses you want to generate for a given prompt. By setting a higher value for n, you can obtain multiple distinct outputs, which can be useful for exploring different perspectives or ideas.
5. stop: A list of strings or tokens that, when encountered, will signal the model to stop generating further text. This parameter is useful when you want to enforce a specific end to the response or prevent the model from generating irrelevant information.
6. echo: If set to true, the API will include the input prompt in the output message. This can be useful for maintaining context when chaining multiple requests or presenting the conversation history in a user interface.

I suggest you become familiar with these parameters are they are the core of successful prompt engineering, especially temperature.

3. Additional Packages: Langchain and Kor

When working with Python, often developers will leverage multiple packages. The same applies to GPT. If you have ever wondered how you can pass structured data (tables, SQL, and more) into ChatGPT for "re-training", Langchain and Kor are what you need to look into. Both packages offer document loaders and much more, such as prompt templates and agents.

Here is a list of Langchain's capabilities:

• Document Loaders: Standard interface for loading documents and integrating various text data sources.
• Utils: Collection of common utilities for enhancing language models, including Python REPLs, embeddings, and search engines.
• Chains: Standard interface for sequences of LLM or utility calls, featuring integrations and end-to-end chains for common applications.
• Indexes: Module for combining LLMs with custom text data, highlighting best practices.
• Agents: Standard interface for LLM decision-making agents, offering agent options and end-to-end examples.
• Memory: Standard interface for the persisting state between chain/agent calls, with multiple memory implementations and examples.
• Chat: Interface for chat models, enabling message-based interaction and integration with other components.

The environment around GPT is green and constantly evolving. I would not be surprised in a matter of weeks there will be much more to work with!

4. Reach Out!

In conclusion, by mastering these core Python skills and technical concepts, you'll be well-equipped to develop innovative GPT-powered products that harness the power of AI to deliver exceptional user experiences.

As we learn more about how to leverage GPT, let's keep sharing knowledge and resources. Don't hesitate to reach out if you have questions or want to brainstorm together!

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