LangChain Quiz

What is the primary problem that LangChain aims to solve?

• Building new Large Language Models from scratch.

• Providing a free alternative to the OpenAI API.

• Simplifying the development of applications around LLMs by providing modular components.

• Creating a new Python library for data analysis.

In LangChain's schema, what does the 'Model' component represent?

• A specific dataset used for training.

• The core reasoning engine, like GPT-4 or Google's Gemini.

• The final output structure of the application.

• A pre-written prompt.

What is the main purpose of a PromptTemplate?

• To hard-code a single, static question for the LLM.

• To convert the LLM's text output into a JSON object.

• To create dynamic and reusable prompt structures with placeholders (variables).

• To directly interact with the LLM API.

How does an 'Output Parser' enhance an LLM's response?

• It checks the LLM's response for factual accuracy.

• It converts the LLM's raw text output into a structured format like a list or JSON.

• It shortens the LLM's response to save on tokens.

• It adds more creativity to the LLM's output.

What is the most fundamental definition of a 'Chain' in LangChain?

• A sequence of operations where the output of one step becomes the input for the next.

• A security protocol for protecting API keys.

• A method for connecting multiple LLMs together.

• The graphical user interface for a LangChain application.

In the Day 1 practical lesson, what does the temperature parameter in llm = OpenAI(temperature=0.7) control?

• The speed of the API response.

• The cost of the API call.

• The model's version.

• The creativity and randomness of the output.

Why is it a best practice to store your API key in a .env file?

• It makes the key easier to remember.

• To keep the key safe and out of your source code, preventing accidental exposure.

• The langchain library requires it to function.

• It allows the API key to be changed dynamically without restarting the program.

What does setting verbose=True in an LLMChain do?

• It makes the final output from the LLM longer and more detailed.

• It prints the internal steps of the chain, like the fully formatted prompt, which is useful for debugging.

• It automatically adds comments to your Python code.

• It enables a more creative (higher temperature) response from the LLM.

A simple LLMChain follows which visual flow?

• [User Input] -> [LLM] -> [PromptTemplate] -> [Output]

• [User Input] -> [PromptTemplate] -> [LLM] -> [Output]

• [PromptTemplate] -> [User Input] -> [LLM] -> [Output]

• [LLM] -> [PromptTemplate] -> [User Input] -> [Output]

What are the three core 'Schemas' or building blocks introduced in Day 1?

• Models, Prompts, and Output Parsers.

• Chains, Agents, and Tools.

• Python, OpenAI, and Dotenv.

• Variables, Functions, and Classes.