This tutorial teaches front-end web development using AI, specifically OpenAI’s API. The course covers building three applications: a movie pitch generator, a GPT-4 chatbot, and a fine-tuned customer support bot for a fictional drone delivery company. Key concepts explored include: prompt engineering, using different OpenAI models, handling API keys securely, and deploying to Netlify. The final project demonstrates fine-tuning a model with custom data to create a chatbot that answers company-specific questions accurately. The instructor emphasizes hands-on coding through numerous challenges.

AI Web Development Study Guide

Quiz

Instructions: Answer the following questions in 2-3 sentences each.

1. What is the primary purpose of the movie pitch app, and what technology does it use to generate movie ideas?
2. Explain the concept of “fine-tuning” in the context of chatbot development.
3. What is a token in the context of OpenAI, and how does the max_tokens property affect text generation?
4. Describe the difference between the zero-shot approach and the few-shot approach in prompt engineering.
5. Why is it important to separate the instruction, examples, and requests when using the few-shot approach in prompt engineering?
6. What is the purpose of the temperature property in the OpenAI API?
7. What is the purpose of using “presence penalty” and “frequency penalty” when working with chatbots, and how do they differ?
8. Why is a Google Firebase database useful for a chatbot application?
9. What does it mean to persist a chat conversation, and how does Firebase achieve this?
10. Explain the purpose of a serverless function, and why it’s important for deploying an application that uses an API with a secret key.

Quiz Answer Key

1. The movie pitch app turns a one-sentence movie idea into a full outline. It uses OpenAI to generate human-standard words and images, creating artwork, titles, synopses, and potential cast members from a single line of input.
2. Fine-tuning involves uploading a custom dataset to train a chatbot to answer specific questions from that data. This skill is essential for using chatbots in specific roles, such as customer service.
3. A token is a small chunk of text, roughly 75% of a word, used by OpenAI for processing. The max_tokens property limits the length of the text output, preventing the model from generating overly long responses.
4. The zero-shot approach uses a simple instruction without any examples to ask for what is needed, while the few-shot approach uses one or more examples to guide the AI in providing more accurate and specific responses.
5. Separating instructions, examples, and requests helps the AI understand that it’s dealing with different parts of the prompt. It allows the AI to recognize the context of the instruction, the expected output format based on examples, and what task it is being asked to complete, thereby improving accuracy.
6. The temperature property controls the randomness of the text output. A lower temperature results in more predictable, factual responses, while a higher temperature results in more creative and varied outputs.
7. Presence penalty encourages the model to talk about new topics by increasing the likelihood of talking about new ideas and concepts rather than staying on one subject, whereas frequency penalty discourages the model from using the same words or phrases repeatedly in a given text generation.
8. A Google Firebase database is useful for a chatbot application because it can store the user’s chat history, which enables the user to start and continue conversations even after the browser is refreshed or closed. This is done by storing the user interactions.
9. Persisting a chat conversation means saving the conversation so that it can be resumed later. Firebase achieves this by storing the conversation data in its database, allowing the application to retrieve and display the conversation when the user returns to the site.
10. A serverless function allows you to run code in a cloud environment without managing servers. It’s important for deploying applications using APIs with secret keys because it hides the API key on the backend, thus preventing it from being exposed in the front-end code.

Essay Questions

Instructions: Answer the following questions in essay format, referencing information from the provided source.

1. Discuss the evolution of prompt engineering techniques presented in the course, from basic instructions to incorporating examples, and explain how these techniques can improve the output of AI models.
2. Explain the significance of controlling token usage and temperature in AI text generation, and how these properties affect the quality and consistency of AI-generated content.
3. Compare and contrast the use of the create completion endpoint and the create chat completion endpoint in the context of AI chatbot development, and discuss the advantages of each approach.
4. Analyze the process of fine-tuning an AI model with custom data, and discuss the steps involved in preparing the data, uploading it to the API, and testing the resulting model.
5. Evaluate the importance of security measures, such as using serverless functions and environment variables, when deploying web applications that use AI APIs with sensitive information.

Glossary of Key Terms

API (Application Programming Interface): A set of protocols and tools for building software applications. It specifies how software components should interact.

Chatbot: A computer program that simulates conversation with human users, either through text or voice interactions.

Completion: The text generated by an AI model as a response to a given prompt.

Environment Variable: A variable with a name and value defined outside the source code of an application, often used to store sensitive information such as API keys.

Epoch: A complete pass through a dataset during training of a machine learning model. One epoch means that each sample in the training dataset has had an opportunity to update the internal model parameters.

Fetch Request: A method in JavaScript used to make HTTP requests to a server, such as retrieving data from an API.

Fine-Tuning: The process of training a pre-trained AI model on a specific dataset to tailor it to a particular task or domain.

Frequency Penalty: An OpenAI setting that reduces the likelihood of the model repeating the same words or phrases.

Few-Shot Approach: A prompt engineering technique that uses one or more examples in the prompt to guide the AI in generating the desired output.

Hallucination: When an AI model generates an incorrect or nonsensical output that may sound plausible.

JSON (JavaScript Object Notation): A lightweight data-interchange format that is easy for humans to read and write, and easy for machines to parse and generate.

JSON-L (JSON Lines): A format where each line is a valid JSON object, often used for storing datasets for machine learning.

Model: An algorithm that has been trained on data to perform a specific task, such as text generation.

Netlify: A web development platform that provides serverless hosting, continuous deployment, and other features.

OpenAI: An artificial intelligence research and deployment company, responsible for creating many large language models, including GPT-4.

Presence Penalty: An OpenAI setting that encourages the model to talk about new topics by reducing the chance of repeating similar subject matter.

Prompt: An input provided to an AI model to generate a response, often in text form.

Serverless Function: A function that executes in a cloud environment, allowing developers to run backend code without managing servers.

Stop Sequence: A sequence of characters in an AI prompt that signals to the model to stop generating text.

Temperature: An OpenAI setting that controls the randomness and creativity of the model’s output.

Token: A small chunk of text used by OpenAI, generally about 75% of a word, for processing and generating text.

Zero-Shot Approach: A prompt engineering technique that uses a simple instruction without any examples.

AI-Powered Web Development Projects

Okay, here is a detailed briefing document summarizing the main themes, ideas, and facts from the provided text.

Briefing Document: AI-Powered Web Development Projects

Overview:

This document summarizes a series of web development projects focused on integrating AI, specifically OpenAI’s models, into different applications. The projects progress from a movie pitch generator to a sophisticated chatbot with persistent storage and a fine-tuned customer service model. The primary focus is on practical application and prompt engineering, with a strong emphasis on understanding how different parameters influence AI responses.

Main Themes & Concepts:

• Leveraging OpenAI API: The core theme is using the OpenAI API to generate text and images for various purposes, including creative writing, question-answering, and image creation.
• Prompt Engineering: The course emphasizes crafting effective prompts to guide AI models towards desired outputs, experimenting with wording, and understanding the impact of examples on the quality and format of responses. Key techniques include:
• Zero-Shot Prompts: Simple instructions without examples.
• Few-Shot Prompts: Providing examples within the prompt to guide the model.
• Using separators: Triple hash marks to separate different parts of a prompt (instructions, examples, input)
• AI Models: The course explores several OpenAI models, highlighting their strengths:
• GPT-3.5 models (text-davinci-003): Good for long text generation and following instructions.
• GPT-4: The latest model, used for advanced chatbots and better contextual understanding.
• Codex models: Designed for generating computer code.
• Tokens and Max Tokens: Tokens are fundamental units of text processed by OpenAI, and max_tokens property controls the length of the generated text. “Roughly speaking, a token is about 75% of a word. So 100 tokens is about 75 words.”
• Temperature: Controls the randomness and creativity of the AI’s output; lower values are for more predictable, factual responses, higher values for more creative and varied outputs. “What temperature does is it controls how often the model outputs a less likely token… giving us some control over whether our completions are safe and predictable on the one hand or more creative and varied on the other hand.”
• Fine-Tuning: Training a model with a custom dataset to achieve specific and focused responses. This section demonstrates using a customer service dataset.
• Chatbot Specifics:
• Conversation Context: Maintaining a conversation history to provide context for subsequent questions.
• Avoiding Repetition: Using frequency_penalty and presence_penalty settings to control how much the chatbot repeats or stays on topic.
• presence_penalty is used to “increase the model’s likelihood of talking about new topics” while frequency_penalty is used to reduce the likelihood of the model “repeating the exact same phrases.”
• API Key Security: Implementing strategies for securely using API keys in front-end projects, such as storing them as environment variables and utilizing Netlify serverless functions to mask API keys during deployment.
• Database Persistence: Utilizing Google Firebase to store chatbot conversation data, allowing users to resume conversations after refreshing or reloading the page.
• Error Handling and User Experience: The projects include loading states, and messages to improve user experience, as well as debugging and error tracking through the console.

Project Highlights and Key Ideas:

• Movie Pitch Generator:Takes a one-sentence movie idea and expands it into a full outline, including title, synopsis, and potential cast.
• Demonstrates basic API interactions with OpenAI.
• Explores techniques to make the responses more detailed and relevant to user input.
• “Know It All” Chatbot:Utilizes the GPT-4 model for natural language conversation.
• Implements conversation persistence using Google Firebase.
• Emphasizes the need for chatbots to maintain context.
• Uses frequency_penalty and presence_penalty to control the chatbot’s output.
• Focuses on having a configurable personality using a system instruction.
• Fine-Tuned Chatbot:Uploads custom data (customer service interactions) to fine-tune a model for specific answers.
• Demonstrates the importance of data formatting, including the use of separators, spacing and stop sequences to format the prompts and completions correctly.
• Explores the concept of epochs, which determine how many times the model iterates through the training data. The text highlights the use of 16 epochs.
• Highlights the use of the OpenAI CLI to prepare the data and run the fine-tuning process in the terminal.
• Secure API Calls:Demonstrates masking the API keys by creating an endpoint via Netlify Functions and calling this endpoint via a fetch request instead of directly calling the OpenAI API from the front end.
• Explores the error that is triggered by a cross-origin request, showcasing that the Netlify serverless function endpoint is secured.

Key Quotes:

• “Studying is more fun and more productive when it’s done together. So, why not interact with fellow students on the Discord community, encourage each other and help each other along.” (Emphasizes collaborative learning).
• “What used to be science fiction is now science fact.” (Highlights the advanced nature of AI)
• “You only get back as much as you put in, so it’s giving us this very boring, generic reply.” (Highlights the importance of effective prompts)
• “An AI model is an algorithm that uses training data to recognize patterns and make predictions or decisions.” (Defines the nature of an AI model)
• “Roughly speaking, a token is about 75% of a word. So 100 tokens is about 75 words.” (Defines tokens)
• “What temperature does is it controls how often the model outputs a less likely token… giving us some control over whether our completions are safe and predictable on the one hand or more creative and varied on the other hand.” (Defines the function of the temperature property)
• “The AI makes up a linguistically plausible answer when it doesn’t know the right answer. And we’ll talk more about hallucinations later in this course.” (Introduces the idea of hallucination in AI)
• presence_penalty is used to “increase the model’s likelihood of talking about new topics” while frequency_penalty is used to reduce the likelihood of the model “repeating the exact same phrases.” (Defines presence and frequency penalties)
• “Each completion should end with a stop sequence to inform the model when the completion ends.” (Highlights the importance of the stop sequence).
• “when you’re working with APIs with secret keys… this solves the really big problem that we have when we’re using APIs with secret keys in front-end projects.” (Highlights the importance of keeping API keys secure).

Next Steps & Future Applications:

• The course encourages building upon these projects, experimenting with different prompts, models, and settings.
• Specific recommendations include:
• Creating more detailed character sketches with image generation.
• Tailoring apps to specific genres.
• Building more robust error handling.
• Fine-tuning models with much larger datasets for production use.
• Building apps with a very specific use case in mind.
• Adding error handling.

Conclusion:

These projects offer a comprehensive introduction to using AI for web development. By emphasizing hands-on experience with prompt engineering, API interactions, and model fine-tuning, this series lays a solid foundation for further exploration and innovation in AI-driven applications. The course also highlights the importance of security, persistence, and creating a good user experience.

Building AI Web Applications with OpenAI

Frequently Asked Questions: AI Development and OpenAI

• What is the main focus of the projects being developed in this course?
• The course focuses on building AI-powered web applications using OpenAI’s large language models (LLMs). These projects include a movie pitch app that generates movie outlines from a single sentence idea, an “Ask Me Anything” chatbot named Know It All, and a customer service chatbot fine-tuned with specific data. These projects emphasize creative use of language models, user interaction, and data persistence. The course also addresses real-world scenarios, like hiding API keys and deploying projects.
• What are the prerequisites for this course?
• The primary prerequisite is a reasonable knowledge of vanilla JavaScript. A basic understanding of fetch requests is also beneficial, but the course will review and explain these concepts step-by-step. The focus will be on the AI aspects of the projects, rather than complicated JavaScript programming.
• How does the movie pitch app work, and what technologies are used?
• The movie pitch app takes a one-sentence movie idea as input and leverages OpenAI’s models to generate a full movie outline, including a title, artwork, a list of stars, and a synopsis. It uses the OpenAI API, and concepts like crafting prompts, tokens, and model training through examples are all covered in the course to build this application. It also demonstrates how to handle asynchronous requests and updates to the user interface using JavaScript.
• What are the different types of AI models mentioned in the course, and which are used?
• The course discusses different types of OpenAI models including:
• GPT-3, GPT-3.5, and GPT-4 models: These are designed for understanding and generating natural language, as well as computer languages. GPT-4 is the latest model and is used for the Know It All chatbot, while text DaVinci 003 (a GPT-3.5 model) is used for other projects.
• Codex models: These models are specifically designed to generate computer code. The course uses the text-davinci-003 model initially, and later upgrades to GPT-4. They emphasize that GPT-3.5 Turbo model can also be used as a substitute for GPT-4.
• What is a token in the context of OpenAI, and how does max_tokens affect a completion?
• In OpenAI, text is broken down into chunks called tokens, with one token being roughly 75% of a word. The max_tokens property controls the maximum length of the text generated by the AI model. It is particularly important to set this value to have control of how much the AI completes, and failure to set this property can cut off responses or cause inconsistent behaviors. The default limit is 16 tokens with the older text-davinci-003 model, and the course recommends setting a higher number.
• What is the few-shot approach to prompt engineering, and why is it useful?
• The few-shot approach involves providing one or more examples of the desired output directly within the prompt to guide the AI model’s generation. By including examples, you can significantly improve the relevance, format, and quality of the AI’s responses. This is compared to the zero-shot approach, where only instructions are given, which often leads to poor quality output for complex requests. The examples are often separated with triple hashtags or triple inverted commas.
• How is data persistence achieved in the Know It All chatbot, and how can the chat be reset?
• The Know It All chatbot uses Google Firebase to store the conversation history, allowing users to continue their chat even after refreshing or reloading the browser. A reset button is implemented, which clears the database and restarts the conversation from the beginning. The course reviews methods for importing the Firebase dependencies, establishing references to the Firebase database, and writing and deleting data to persist and reset chat sessions.
• What is fine-tuning, and what steps are involved in creating a fine-tuned model?
• Fine-tuning involves training a pre-existing large language model with a specific dataset, to get more targeted responses. The course uses a CSV formatted dataset that contains prompt-completion pairs to fine tune a customer service bot. The steps involved in fine-tuning a model include setting up a command-line interface (CLI) with Python, preparing the data using OpenAI’s data preparation tool (which will convert it into JSONL format), and using the CLI to upload and train the model on the prepared data. Also, the course addresses the concept of epochs and using the CLI to increase the epochs when creating a fine-tuned model, as well as setting the presence and frequency penalty to reduce repetition in output. Finally, the course addresses hiding the API key in the deployed project using Netlify environment variables and using serverless functions for making calls to the API to hide these keys.

Movie Pitch App: OpenAI API Integration

The Movie Pitch app is designed to generate creative movie ideas using the OpenAI API. Here’s a breakdown of its key features and development process:

• Core Functionality: The app takes a one-sentence movie idea from the user and, using the power of OpenAI, generates a full movie outline, including:
• A title
• A synopsis
• Artwork for the cover
• A list of stars
• Technology Used: The app utilizes the OpenAI API and various models including the text DaVinci 003. It also incorporates HTML, CSS, and JavaScript.
• Development Process:Initial Setup: The app starts with a basic HTML structure, including a text area for user input and designated areas for displaying the AI-generated content.
• API Integration: The app uses fetch requests to communicate with the OpenAI API, sending prompts and receiving responses.
• Prompt Engineering: The course emphasizes the importance of crafting effective prompts to guide the AI’s responses. This involves:
• Understanding how to use tokens
• Tweaking prompts to get desired results
• Using examples to train the model
• Using a zero-shot approach, where a simple instruction is given
• Moving to a few-shot approach by adding one or more examples to the prompt
• Using separators to distinguish instructions and examples
• Using techniques to control the length of the output such as specifying the number of words or using max tokens
• Personalized Responses: The app is designed to provide personalized responses based on the user’s input.
• Text Extraction: The app extracts the names of actors from the generated synopsis.
• Image Generation: The app also utilizes the OpenAI API to generate images based on the movie concept. This involves converting the synopsis and title into a suitable image prompt.
• Key Concepts:AI Models: The course introduces different OpenAI models, including GPT-3, GPT-3.5, and GPT-4, as well as Codex models. It explains that these models are algorithms that use training data to recognize patterns and make decisions or predictions.
• Temperature: The course also covers the concept of temperature, a property used to control the creativity and predictability of AI completions.
• Tokens: The course explains how the OpenAI API uses tokens and how they affect the length and cost of API requests.
• Deployment Considerations:The course discusses the importance of securing API keys when deploying front-end projects. It uses Netlify to safely store the API key on a server.
• Potential Improvements:The course suggests that the code could be refactored to improve reusability, and to focus more on AI and less on Javascript.
• The course also suggests exploring the idea of having the AI generate a script for the movie
• The course also suggests tailoring the app to a specific genre
• Warnings:
• The course emphasizes that while developing locally the API key is visible on the front end and anyone could steal the API key.
• The course suggests not sharing the project with the API key or publishing it to GitHub without ignoring the API key because that will compromise the API key.

In summary, the Movie Pitch app is an interactive project that demonstrates how to use the OpenAI API to generate creative movie concepts. It introduces core concepts in AI and prompt engineering and highlights best practices in building and deploying AI-powered applications.

OpenAI API Guide

The OpenAI API is a central component in building AI-powered applications, as demonstrated in the Movie Pitch app. Here’s a breakdown of key aspects of the OpenAI API as discussed in the sources:

• API Key: To use the OpenAI API, you need an API key, which can be obtained by signing up on the OpenAI website. The API key needs to be kept secret, and the sources caution against sharing it or publishing it without taking precautions to protect it.
• Endpoints: The OpenAI API has different endpoints for different tasks.
• Completions Endpoint: This endpoint is used to generate text based on a prompt. It is central to the API. The API takes a prompt and sends back a “completion” that fulfills the request.
• Chat Completions Endpoint: This endpoint is designed for chatbot applications and is used with models like GPT-4 and GPT 3.5 Turbo.
• Create Image Endpoint: This endpoint is used to generate images based on text prompts.
• Models:
• OpenAI has various models geared toward different tasks.
• GPT Models: GPT-3, GPT-3.5, and GPT-4 are used for understanding and generating natural language and can also generate computer languages. GPT-4 is the newest and most advanced model.
• Codex Models: These models are specifically designed to generate computer code.
• The models vary in terms of complexity, speed, cost, and the length of the output they provide.
• The sources suggest starting with the best model available and then downgrading to save on time and cost where possible.
• Fine-tuned models can be created using a custom dataset.
• Prompts:
• A prompt is a request for the OpenAI API. Prompts can be simple or complex.
• Prompt engineering is a key skill when working with the OpenAI API. It involves crafting effective prompts to guide the AI’s responses.
• The sources describe three approaches to prompt design:
• Zero-shot approach: This involves giving a simple instruction or asking a question.
• Few-shot approach: This involves adding one or more examples to the prompt to help the AI understand what is required.
• Using separators like triple hashes (###) or triple inverted commas to separate instructions and examples within a prompt.
• Good prompt design is key to controlling the length of the output and ensuring the text from OpenAI is of the desired length.
• Tokens:OpenAI breaks down chunks of text into tokens for processing.
• A token is roughly 75% of a word.
• The number of tokens used impacts the cost and processing time of API requests.
• The max tokens property can be used to limit the length of the completion. If not set, the model defaults to a low number, which may cause the text to be cut short.
• Temperature:The temperature setting controls how often the model outputs a less likely token.
• It can be used to control how creative and varied a completion is.
• Usage and Cost:
• OpenAI provides some free credit when you sign up, but after that, it uses a pay-as-you-go model.
• The cost of using the API depends on the model, the number of tokens, and the number of images generated.
• Authentication: The API requires authentication via the API key in the header of the request.
• Security: The API key should be kept secret. It is important not to expose it on the front end when deploying applications. The sources suggest using a serverless function to hide the API key from the front end code.

In summary, the OpenAI API is a versatile tool for building a wide range of AI-powered applications. It offers different models, endpoints, and configuration options to perform tasks like text generation, image creation, and creating chatbots. Understanding how to use tokens, craft effective prompts, and secure API keys are crucial for working with the OpenAI API.

Building Chatbots with the OpenAI API

Creating a chatbot using the OpenAI API involves several key steps, from setting up the API to fine-tuning the model. Here’s a breakdown of the process, based on the sources:

• API Setup: The process begins with setting up the OpenAI API, which involves obtaining an API key and understanding the different endpoints.
• For chatbots, the Chat Completions endpoint is used. This endpoint is designed to handle conversational exchanges.
• The API key should be kept secure and not exposed on the front end.
• Model Selection: The choice of model is crucial for a chatbot’s performance.
• GPT-4 is the most advanced model at the time of recording and is well-suited for chatbot applications.
• GPT-3.5 Turbo is also a very capable model that can be used as an alternative when access to GPT-4 is limited.
• The models vary in terms of their ability to generate human-like text, their cost, and their speed.
• Conversation Handling:
• Chatbots require a memory of past interactions to maintain context and provide coherent responses.
• Unlike the text DaVinci 003 model, the models used with the Chat Completions endpoint do not have a memory of past completions.
• To maintain context, the entire conversation history must be sent with each API request.
• The conversation is stored in an array of objects, where each object represents a message in the conversation.
• The first object in the array is an instruction that tells the chatbot how to behave. This object has a role key with a value of system and a content key with a string containing the instruction.
• Subsequent objects store the user’s input and the API’s responses. These objects have a role key with either a value of user or assistant and a content key with a string containing the message.
• API Requests:
• API requests are sent to the Chat Completions endpoint with the createChatCompletion method, along with a messages property holding the conversation array.
• The API response is then added to the conversation array to maintain context for the next request.
• The API request also needs to specify a model property.
• Chatbot Personality:
• A chatbot’s personality can be customized through the instruction object at the beginning of the conversation array.
• This object can be used to tell the chatbot to be sarcastic, funny, practical or any other personality.
• It can also be used to control the length of the responses or simplify the language.
• Response Handling:
• The chatbot’s response from the API needs to be rendered to the DOM and added to the conversation array.
• The response from the API will include the role and the content.
• Presence and Frequency Penalties:
• Presence penalty can be used to control how likely a chatbot is to talk about new topics.
• Frequency penalty can be used to control how repetitive the chatbot is in its choice of words and phrases.
• The sources suggest not going over one and not going under zero for either setting.
• Data Persistence:To make the conversation persistent, a database can be used to store the conversation array.
• The sources use Google Firebase for this purpose.
• The conversation is stored in the database and is loaded into the app when the page loads.
• The user can reset the conversation using a button that removes the data from the database and clears the display.
• Fine-TuningChatbots can be fine-tuned with a custom dataset to answer specific questions about a company.
• A fine-tuned model is trained on a dataset that is prepared in JSONL format.
• The data set includes prompts and completions and is prepared using the OpenAI CLI tool.
• When using a fine-tuned model, the Completions endpoint and createCompletion method is used. The API request should also have a prompt property rather than the messages property used by models such as GPT-4.
• When working with a fine-tuned model it is important to use a stop sequence and to end the prompt with a separator. The sources used a space and an arrow (->) as a separator and a new line character (\n) as a stop sequence.
• The temperature setting can be used to control how creative and varied the completions are. If factual answers are desired it should be set to 0.

In summary, creating a chatbot involves using the OpenAI API, selecting the appropriate model, managing conversation context, and handling responses. Additional steps such as fine-tuning and data persistence can be added to enhance the bot’s capabilities.

Fine-Tuning AI Models

Fine-tuning AI models is a way to customize them for specific tasks and datasets, as discussed in the sources. Here’s a breakdown of key concepts related to fine-tuning:

• Purpose of Fine-tuning:
• General-purpose AI models, like those trained by OpenAI, are trained on publicly available data. While this works well for general tasks such as Q&A or translation, it isn’t ideal for tasks that require specific information.
• Fine-tuning is used to address the limitations of general models by providing them with a custom dataset. This allows them to answer questions specific to a company or domain.
• Fine-tuning enables models to provide accurate responses and avoid generating incorrect answers, also called “hallucinations”.
• Data Preparation:
• High-quality, vetted data is essential for effective fine-tuning. The data should be relevant to the specific task for which the model is being fine-tuned.
• The sources recommend at least a few hundred examples, and possibly thousands, for optimal results.
• Data is formatted as pairs of prompts and completions.
• The data should be formatted as JSON-L, where each line is a valid JSON object.
• OpenAI’s data preparation tool can be used to convert data from CSV to JSON-L format.
• The tool adds a separator to the end of each prompt, a whitespace to the beginning of each completion, and a stop sequence to the end of each completion.
• Fine-tuning Process:
• The fine-tuning process is initiated using the OpenAI command-line interface (CLI) tool.
• The CLI tool takes the training data file and a base model as inputs.
• The base model is the starting point, and the model is customized using the training data.
• The sources used the DaVinci model as a base model for fine-tuning.
• The fine-tuning process takes time, ranging from minutes to hours.
• The CLI tool uses a command like openai fine_tunes.create -t <TRAINING_FILE> -m <BASE_MODEL>.
• Epochs:
• Epochs refers to the number of times the model cycles through the training data.
• The default number of epochs is four, which might be sufficient for larger datasets but not for smaller ones.
• The number of epochs can be specified in the fine-tuning command using the flag –n_epochs <NUMBER_OF_EPOCHS>. For smaller datasets, the sources recommend using 16 epochs for improved results.
• Using a Fine-Tuned Model:
• After fine-tuning, a unique model ID is provided.
• The fine-tuned model can then be used in an application. The sources show how a chatbot was customized by using a fine-tuned model.
• Fine-tuned models use the Completions endpoint and the createCompletion method.
• The API request should have a prompt property rather than a messages property.
• It is also important to use a stop sequence to prevent the bot from continuing the conversation on its own. The sources used a new line character (\n) as a stop sequence and a space and an arrow (->) as a separator.
• Benefits of Fine-Tuning:
• Fine-tuning allows the model to provide accurate and specific responses tailored to the training dataset.
• It can improve a model’s ability to understand context and nuance.
• Fine-tuning is useful when it is important for an AI model to be able to say “I don’t know” rather than make up an answer.
• Fine-tuning can enable the model to avoid generating incorrect answers or “hallucinations”.

In summary, fine-tuning involves preparing a custom dataset, training a model on this data, and using the new model in an application. Fine-tuning enables the AI model to give more specific and accurate responses than it could have given without fine-tuning.

Securing OpenAI API Keys

API key security is a crucial aspect of working with services like OpenAI, as highlighted in the sources. Here’s a breakdown of the key points related to API key security:

• Risk of Exposure: API keys should be kept secret because they provide access to the associated service. If an API key is exposed, unauthorized individuals could potentially use the service, leading to unexpected charges or other misuse.
• API keys can be exposed if they are included directly in front-end code.
• When developing locally, the API key may be visible in the code, but this is acceptable for local development.
• Sharing a project with an API key or publishing to GitHub without hiding the API key will compromise the API key.
• Hiding API Keys: To prevent API key exposure, it’s important to keep them out of the client-side code. The sources recommend the following strategies for hiding API keys:
• Server-Side Storage: API keys should be stored on a server, rather than on the front end. This ensures that they are not visible to users.
• Environment Variables: API keys can be stored in environment variables on a server. This prevents them from being directly included in the code.
• When using Netlify, environment variables can be set in the site settings.
• Serverless Functions: Serverless functions can be used as an intermediary between the front end and the API. The serverless function can have access to the API key, while the front end does not.
• The serverless function makes the API call and returns the data to the front end, without exposing the API key.
• Best Practices:
• API keys should be treated like passwords and kept confidential.
• It is important to avoid sharing API keys or publishing them to public repositories.
• When working with API keys, it’s important to be mindful of what you’re doing and to ensure that the keys are not being shared inadvertently.
• API keys should only be stored in secure locations.
• When using an API key on a front-end project, it’s vital to take steps to hide it before sharing the project.
• Consequences of Exposure:
• If an API key is exposed, unauthorized users could potentially use it, which could result in unexpected charges.
• Compromised API keys can be used for malicious purposes.
• If an API key is lost, it is best to delete it and create a new one.
• Netlify Specific Security:
• When using Netlify, a serverless function will only accept requests from its own domain, so other domains cannot make fetch requests to that serverless function.

In summary, API key security is paramount when working with APIs. Storing API keys on a server, using environment variables, and utilizing serverless functions are effective strategies for hiding API keys and preventing unauthorized access.

By Amjad Izhar
Contact: amjad.izhar@gmail.com
https://amjadizhar.blog

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