Module 3 Wrap-up: Your Reusable Library
Hands-on: Build a reusable prompt library and implement a few-shot dynamic classifier.
Module 3 Wrap-up: The Prompt Architect
You have learned that prompts are the "Programs" of the LLM era. By using Templates, Messaging Abstractions, and Few-Shotting, you have moved from "Chatting" with AI to Engineering it.
Hands-on Exercise: The Dynamic Persona Library
1. The Goal
Create a script that has 3 different SystemMessage templates (Teacher, Pirate, Debugger). Allow the user to pick one, and then ask a question. The script must format the correct ChatPromptTemplate and return the model's response.
2. The Implementation Plan
- Create a dictionary of templates.
- Initialize a
ChatPromptTemplatewith the chosen persona. - Invoke the model.
Module 3 Summary
- Pillars: Context, Task, and Format.
- PromptTemplate: Reusable strings with variables.
- ChatPromptTemplate: The standard for Multi-Message AI interaction.
- Few-Shotting: Providing examples to enforce pattern matching.
- Hub/Versioning: Managing prompts as stable software artifacts.
Coming Up Next...
In Module 4, we learn how to link these prompts and models together into Chains. We will move beyond single requests and build multi-step workflows using the LangChain Expression Language (LCEL).
Module 3 Checklist
- I have used
{variable}syntax in a local prompt. - I can explain why
ChatPromptTemplateuses tuples("human", "..."). - I have pulled at least one prompt from the LangChain Hub.
- I have built a Few-Shot prompt with at least 2 examples.
- I understand how Delimiters prevent Prompt Injection.