Rapid Application Development

Rapid Application Development

Using Large Language Models in a single day.

As businesses and developers increasingly embrace AI-driven solutions, the demand for rapid prototyping and application development has never been greater. Large Language Models (LLMs) like OpenAI's GPTs have become indispensable tools for solving complex problems, building sophisticated software, and streamlining workflows.

This one-day intensive course is designed to empower participants with the ability to integrate LLMs into their development pipelines, leveraging their unprecedented capabilities for natural language understanding, code generation, and automation.

Participants will learn how to efficiently develop real-world applications using LLMs, while reducing development time and maximizing scalability. This course focuses on practical, industry-relevant techniques for utilizing state-of-the-art tools, giving participants a competitive edge in their projects.

Learning Outcomes

By the end of this course, participants will:

• Understand the principles of rapid application development (RAD) and its intersection with large language models.
• Gain hands-on experience in using cutting-edge LLM platforms and frameworks.
• Learn to fine-tune pre-trained LLMs for specific application needs.
• Design and deploy LLM-driven applications efficiently using modern tooling and hardware.
• Explore best practices for integrating LLMs into production environments.
• Address challenges such as scalability, latency, ethical considerations, and GPU optimization.

Prerequisites

To successfully participate in this course, learners must have:

• Advanced Python skills (including knowledge of libraries such as PyTorch, TensorFlow, and Hugging Face).
• Proficiency in Linux environments (for handling deployment workflows and command-line tools).
• Access to GPU resources with at least 20k CUDA cores to support high-performance model training and inference. Colab Pro is an option as well.
• HuggingFace Account: Must be a verified member of the https://huggingface.co/ community with permissions for LLMs of choice download.
• Pro access to OpenAI’s API and Chatbot.
• Familiarity with containerization tools (e.g., Docker) and cloud platforms.
• Google account for resource sharing and notebook spin ups.

Summarized Training Outline

NOTE: How much of the following can be covered depend on the performance of the participants and environmental factors such as (but not limited to) hardware limitations, internet connections etc. The trainer shall make all efforts to cover all of the below topics but it depends on the final capacity of the participating group.

1. Foundations of Rapid Application Development with LLMs

• Overview of Rapid Application Development (RAD): Principles and objectives.
• Role of LLMs in the RAD paradigm.
  • Automating code generation.
  • Accelerating prototyping cycles.
  • Reducing development costs and human error.
• Real-world use cases of LLM-driven RAD.

2. Understanding the LLM Ecosystem

• A deep dive into Large Language Models:
  • Architecture basics (Transformers, self-attention mechanisms).
  • Key models: GPT, LLaMA, Nemotron etc..
• Open-source vs proprietary LLMs: Which to choose and when.
• Overview of major LLM development frameworks:
  • Hugging Face Transformers.
  • LangChain for application orchestration.
  • OpenAI APIs and fine-tuning methods.

3. Setting Up Your Development Environment

• Hardware requirements for LLM development.
  • Optimizing GPU usage for training and inference.
  • Using NVIDIA A100, H100 GPUs, and multi-GPU setups.
• Software stack installation:
  • Python environments (e.g., Conda, venv).
  • Installing CUDA and other dependencies.
  • Dockerized environments for reproducibility.
• Accessing cloud GPU resources via cloud.

4. Building and Deploying LLM-Powered Applications

• Design principles for LLM-based applications:
  • Prompt engineering and input optimization.
  • Managing context windows for better performance.
• Rapid prototyping workflows:
  • Using LangChain to chain LLM outputs.
  • Integrating LLMs with Python backends using Flask and FastAPI.

This course is perfect for advanced developers seeking to revolutionize their workflows by integrating the cutting-edge power of Large Language Models into their applications. With the guidance of an instructor with over 30 years of industry experience, participants will leave with actionable skills and a solid foundation to drive innovation in their projects.