DeepLearning.AI Building LLM Applications with Prompt Engineering (NVIDIA)

Description

DeepLearning.AI Building LLM Applications with Prompt Engineering (NVIDIA) is designed for enterprises eager to integrate large language models (LLMs) into their products and applications. The course covers modern prompt engineering techniques, foundational for advanced LLM methods like Retrieval-Augmented Generation (RAG) and Parameter-Efficient Fine-Tuning (PEFT). Participants will work with NVIDIA's NIM language model, powered by Llama-3.1, and the LangChain library.

The course aims to equip learners with the skills to build a range of LLM-based applications. Key topics include iterative prompt engineering, LangChain workflows, and application code for generative tasks, document analysis, and chatbot applications. The course is suitable for intermediate Python developers, though a solid understanding of LLM fundamentals is helpful.

Participants will learn to use LangChain Expression Language (LCEL) for composing chains and creating parallel workflows. The course also covers few-shot prompting, chain-of-thought prompting, and structured data generation using Pydantic classes and LangChain's JsonOutputParser. Learners will create LLM agents capable of using external tools and APIs.

Upon successful completion, participants receive an NVIDIA DLI certificate, recognizing their competency in LLM applications. The course requires a desktop or laptop with Chrome or Firefox and provides access to a GPU-accelerated server. The course is conducted in English, and enrollment ends on July 7, with access until December 31.

DeepLearning.AI Building LLM Applications with Prompt Engineering (NVIDIA)'s Core Features

• Iterative prompt engineering

• LangChain workflows

• Generative tasks

• Document analysis

• Chatbot applications

• LangChain Expression Language

• Few-shot prompting

• Chain-of-thought prompting

• Structured data generation

• LLM agent creation

How to use DeepLearning.AI Building LLM Applications with Prompt Engineering (NVIDIA)?

1. 1

   Configure: Set up your environment with NVIDIA's NIM and LangChain

2. 2

   Use: Apply prompt engineering techniques to build applications

3. 3

   Optimise: Enhance workflows using LangChain Expression Language

4. 4

   Review: Complete assessments and earn certification

DeepLearning.AI Building LLM Applications with Prompt Engineering (NVIDIA)'s Use Cases

• Text Generation
• Document Analysis
• Chatbot Assistants
• Data Extraction
• Tool Integration

FAQ from DeepLearning.AI Building LLM Applications with Prompt Engineering (NVIDIA)

• What is the focus of the NVIDIA LLM course?

  The course focuses on prompt engineering techniques for building applications with large language models.

• What models are used in the course?

  The course uses NVIDIA's NIM model and the open-source Llama-3.1 model.

• What is LangChain?

  LangChain is a library used to organize and compose workflows for LLM applications.

• Who is the course intended for?

  The course is intended for intermediate Python developers with an understanding of LLM fundamentals.

• What certificate is awarded upon completion?

  Participants receive an NVIDIA DLI certificate recognizing their competency in LLM applications.

• What are the hardware requirements?

  A desktop or laptop capable of running Chrome or Firefox, with access to a GPU-accelerated server.

• What language is the course conducted in?

  The course is conducted in English.

• When does enrollment end?

  Enrollment ends on July 7, with access until December 31.