Combining retrieved context with LLM in Prompt Engineering / GenAI - Cheat Sheet & Quick Revision

Recall & Review

beginner

What does 'retrieved context' mean when working with a Large Language Model (LLM)?

Retrieved context is extra information or data fetched from an external source to help the LLM give better and more accurate answers.

beginner

Why do we combine retrieved context with an LLM's input?

We combine retrieved context with the LLM's input to provide it with relevant facts or details it might not remember, improving the quality of its responses.

intermediate

Name one common method to combine retrieved context with an LLM.

One common method is to prepend the retrieved context as extra text before the user's question, so the LLM reads it all together.

intermediate

What is a potential challenge when combining retrieved context with an LLM?

A challenge is that the combined input might become too long, exceeding the LLM's maximum token limit, which can cause errors or cut off information.

beginner

How can combining retrieved context with an LLM improve real-life applications?

It helps in tasks like customer support or research by giving the LLM up-to-date or specific information, making answers more useful and trustworthy.

What is the main purpose of adding retrieved context to an LLM's input?

A To make the LLM run faster

B To provide extra relevant information for better answers

C To reduce the size of the input text

D To confuse the LLM

Adding retrieved context gives the LLM more relevant facts, helping it generate better responses.

Which of the following is a common way to combine retrieved context with an LLM?

A Replacing the question with context

B Ignoring the context completely

C Sending context after the answer

D Prepending the context before the question

Prepending the context before the question lets the LLM read all information together.

What can happen if the combined input with context is too long for the LLM?

A The LLM may cut off some information or error out

B The LLM will answer faster

C The LLM will ignore the question

D The LLM will learn new facts

LLMs have token limits; exceeding them can cause truncation or errors.

Why is retrieved context important for up-to-date answers?

A Because LLMs may not know recent information

B Because LLMs always know everything

C Because context slows down the model

D Because context replaces the LLM

LLMs are trained on past data and may miss recent facts; context fills that gap.

Which real-life task benefits from combining retrieved context with an LLM?

A Playing video games

B Drawing pictures

C Customer support with specific product info

D Listening to music

Customer support needs accurate, specific info, which context helps provide.

Explain how combining retrieved context with an LLM improves the quality of its responses.

Think about what the LLM knows and what it might miss.

Describe one challenge when adding retrieved context to an LLM input and how it might be handled.

Consider the LLM's maximum input size.