BLAPE Outline - Building LLM Applications with Prompt Engineering

Building LLM Applications with Prompt Engineering (BLAPE) – Outline

Detailed Course Outline

Orient to the main worshop topics, schedule and prerequisites.
Learn why prompt engineering is core to interacting with Large Languange Models (LLMs).
Discuss how prompt engineering can be used to develop many classes of LLM-based applications.
Learn about NVIDIA LLM NIM, used to deploy the Llama 3.1 LLM used in the workshop.

Get familiar with the workshop environment.
Create and view responses from your first prompts using the OpenAI API, and LangChain.
Learn how to stream LLM responses, and send LLMs prompts in batches, comparing differences in performance.
Begin practicing the process of iterative prompt development.
Create and use your first prompt templates.
Do a mini project where to perform a combination of analysis and generative tasks on a batch of inputs.

Learn about LangChain runnables, and the ability to compose them into chains using LangChain Expression Language (LCEL).
Write custom functions and convert them into runnables that can be included in LangChain chains.
Compose multiple LCEL chains into a single larger application chain.
Exploit opportunities for parallel work by composing parallel LCEL chains.
Do a mini project where to perform a combination of analysis and generative tasks on a batch of inputs using LCEL and parallel execution.

Learn about two of the core chat message types, human and AI messages, and how to use them explictly in application code.
Provide chat models with instructive examples by way of a technique called few-shot prompting.
Work explicitly with the system message, which will allow you to define an overarching persona and role for your chat models.
Use chain-of-thought prompting to augment your LLMs ability to perform tasks requiring complex reasoning.
Manage messages to retain conversation history and enable chatbot functionality.
Do a mini-project where you build a simple yet flexible chatbot application capable of assuming a variety of roles.

Explore some basic methods for using LLMs to generate structured data in batch for downstream use.
Generate structured output through a combination of Pydantic classes and LangChain's `JsonOutputParser`.
Learn how to extract data and tag it as you specify out of long form text.
Do a mini-project where you use structured data generation techniques to perform data extraction and document tagging on an unstructured text document.

Create LLM-external functionality called tools, and make your LLM aware of their availability for use.
Create an agent capable of reasoning about when tool use is appropriate, and integrating the result of tool use into its responses.
Do a mini-project where you create an LLM agent capable of utilizing external API calls to augment its responses with real-time data.