Working With OpenAI and Prompt Engineering for React Developers

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In this workshop we'll take a tour of applied AI from the perspective of front end developers, zooming in on the emerging best practices when it comes to working with LLMs to build great products. This workshop is based on learnings from working with the OpenAI API from its debut last November to build out a working MVP which became PowerModeAI (A customer facing ideation and slide creation tool).


In the workshop they'll be a mix of presentation and hands on exercises to cover topics including:


- GPT fundamentals

- Pitfalls of LLMs

- Prompt engineering best practices and techniques

- Using the playground effectively

- Installing and configuring the OpenAI SDK

- Approaches to working with the API and prompt management

- Implementing the API to build an AI powered customer facing application

- Fine tuning and embeddings

- Emerging best practice on LLMOps

This workshop has been presented at React Advanced 2023, check out the latest edition of this React Conference.

FAQ

A Large Language Model (LLM) is a type of AI model that processes and generates human-like text based on the data it has been trained on. It uses mechanisms like self-attention to understand context and generate responses that are contextually relevant.

LLMs offer several advantages including handling multiple tasks with a single model, reducing barriers to AI implementation, and providing quick setup and iteration for AI applications. They are also capable of improving performance and productivity compared to traditional machine learning techniques.

LLMs can be fine-tuned by training them on a specific dataset tailored to the desired output. This process involves preparing and uploading training data, creating a fine-tune model through an API, and then using this model to generate more specific responses.

Common use cases for LLMs include chatbots, content generation, language translation, sentiment analysis, and more. They are particularly useful for applications that require understanding and generating human-like text.

Limitations of LLMs include context window size constraints, potential for generating incorrect or biased information (hallucinations), high computational costs, and lack of transparency in data training and processing.

Handling large documents with LLMs can be managed through techniques like document chunking, summarization to reduce input size, and retrieval-augmented generation, which involves using external retrieval systems to fetch relevant document snippets during the generation process.

Richard Moss
Richard Moss
98 min
30 Oct, 2023

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Video Summary and Transcription
This Workshop focuses on implementing software using the SDK and API layer, with a focus on JavaScript and React development. It covers topics such as advancements in AI and language models, unlocking language user interfaces, generative AI, large language models and hallucinations, prompt engineering, improving model performance, developing prompts and an iterative approach, prompt development and management, interesting use cases, using the playground and understanding tokens, streaming text and JSON, exploring example implementations, fine-tuning and use cases, limitations and emerging architectures, working with documents and LangChain, embeddings, agents, and development tools, scraping data and fine-tuning, and using LLM for data transformation.
Video transcription and chapters available for users with access.

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