Prompting

Detailed, explicit instructions produce better results than open-ended prompts: You can think about giving explicit instructions as using rules and restrictions to how Llama 2 responds to your prompt.

Only use academic papers.

Never give sources older than 2020.

If you don't know the answer, say that you don't know.

Here's an example of giving explicit instructions to give more specific results by limiting the responses to recently created sources:

Explain the latest advances in large language models to me.
#  More likely to cite sources from 2017

Explain the latest advances in large language models to me. Always cite your sources.
Never cite sources older than 2020.
# Gives more specific advances and only cites sources from 2020

A shot is an example or demonstration of what type of prompt and response you expect from a large language model. This term originates from training computer vision models on photographs, where one shot was one example or instance that the model used to classify an image.

Large language models like Meta Llama are capable of following instructions and producing responses without having previously seen an example of a task. Prompting without examples is called "zero-shot prompting".

Text: This was the best movie I've ever seen! 
The sentiment of the text is:

Text: The director was trying too hard.
The sentiment of the text is:

Adding specific examples of your desired output generally results in a more accurate, consistent output. This technique is called "few-shot prompting". In this example, the generated response follows our desired format that offers a more nuanced sentiment classifier that gives a positive, neutral, and negative response confidence percentage.

You are a sentiment classifier. For each message, give the percentage of positive/netural/negative. Here are some samples:
Text: I liked it
Sentiment: 70% positive 30% neutral 0% negative
Text: It could be better
Sentiment: 0% positive 50% neutral 50% negative
Text: It's fine

Sentiment: 25% positive 50% neutral 25% negative

Text: I thought it was okay

Text: I loved it!

Text: Terrible service 0/10

Creating prompts based on the role or perspective of the person or entity being addressed. This technique can be useful for generating more relevant and engaging responses from language models.

1. Improves relevance: Role-based prompting helps the language model understand the role or perspective of the person or entity being addressed, which can lead to more relevant and engaging responses.
2. Increases accuracy: Providing additional context about the role or perspective of the person or entity being addressed can help the language model avoid making mistakes or misunderstandings.

1. Requires effort: Requires more effort to gather and provide the necessary information about the role or perspective of the person or entity being addressed.

You are a virtual tour guide currently walking the tourists Eiffel Tower on a night tour. Describe Eiffel Tower to your audience that covers its history, number of people visiting each year, amount of time it takes to do a full tour and why do so many people visit this place each year.

Involves providing the language model with a series of prompts or questions to help guide its thinking and generate a more coherent and relevant response. This technique can be useful for generating more thoughtful and well-reasoned responses from language models.

1. Improves coherence: Helps the language model think through a problem or question in a logical and structured way, which can lead to more coherent and relevant responses.
2. Increases depth: Providing a series of prompts or questions can help the language model explore a topic more deeply and thoroughly, potentially leading to more insightful and informative responses.

1. Requires effort: The chain of thought technique requires more effort to create and provide the necessary prompts or questions.

You are a virtual tour guide from 1901. You have tourists visiting Eiffel Tower. Describe Eiffel Tower to your audience. Begin with
1. Why it was built
2. Then by how long it took them to build
3. Where were the materials sourced to build
4. Number of people it took to build
5. End it with the number of people visiting the Eiffel tour annually in the 1900's, the amount of time it completes a full tour and why so many people visit this place each year.
Make your tour funny by including 1 or 2 funny jokes at the end of the tour.

LLMs are probabilistic, so even with Chain-of-Thought, a single generation might produce incorrect results. Self-Consistency introduces enhanced accuracy by selecting the most frequent answer from multiple generations (at the cost of higher compute):

John found that the average of 15 numbers is 40.
If 10 is added to each number then the mean of the numbers is?
Report the answer surrounded by three backticks, for example: ```123```

Running the above several times and taking the most commonly returned value for the answer would make use of the self-consistency approach.

Common facts are generally available from today's large models out-of-the-box (i.e. using just the model weights). More specific data is unlikely to be available though E.g.:

What is the capital of  California?
# The capital of California is Sacramento...

What was the temperature in Menlo Park on December 12th, 2023?
# I'm just an AI, I don't have have access to real-time or historical weather data...

Retrieval-Augmented Generation, or RAG, describes the practice of including information in the prompt that has been retrieved from an external database. It's an effective way to incorporate facts into your LLM application and is more affordable than fine-tuning which might also negatively impact the foundational model's capabilities.

This could be as simple as a lookup table or as sophisticated as a vector database containing all of your company's knowledge:

Given the following information about temperatures in Menlo Park:
2023-12-11 :  52 degrees Fahrenheit
2023-12-12 :  51 degrees Fahrenheit
2023-12-13 :  55 degrees Fahrenheit
What was the temperature in Menlo Park on 2023-12-12?

# Sure! The temperature in Menlo Park on 2023-12-12 was 51 degrees Fahrenheit.

What was the temperature in Menlo Park on 2023-07-18 ?

# Sorry, I don't have information about the temperature in Menlo Park on 2023-07-18. The information provided only includes temperatures for December 11th, 12th, and 13th of 2023.

LLMs, by nature, aren't great at performing calculations. While LLMs are bad at arithmetic, they're great for code generation. Program-Aided Language leverages this fact by instructing the LLM to write code to solve calculation tasks.

Python code to calculate: ((-5 + 93 * 4 - 0) * (4^4 + -7 + 0 * 5))

Here is the Python code to calculate the given expression:

((-5 + 93 * 4 - 0) * (4**4 + -7 + 0 * 5))

# Calculate the expression inside the parentheses
a = -5 + 93 * 4 - 0
b = 4**4 + -7 + 0 * 5

# Multiply a and b
result = a * b

# Print the result
print(result)

Using the code directly provides the correct result.

A common challenge is generating a response without extraneous tokens (e.g. "Sure! Here's more information on...").

By combining a role, rules and restrictions, explicit instructions, and an example, the model can be prompted to generate the desired response.

You are a robot that only outputs JSON.
You reply in JSON format with the field 'zip_code'.
Example question: What is the zip code of the Empire State Building? 
Example answer: {'zip_code': 10118}
Now here is my question: What is the zip code of Menlo Park?

# "{'zip_code': 94025}"

Using the code directly provides the correct result.

Here are some examples of how a language model might hallucinate and some strategies for fixing the issue:

A language model is asked to generate a response to a question about a topic it has not been trained on. The language model may hallucinate information or make up facts that are not accurate or supported by evidence.

A language model is asked to generate a response to a question that requires a specific perspective or point of view. The language model may hallucinate information or make up facts that are not consistent with the desired perspective or point of view.

A language model is asked to generate a response to a question that requires a specific tone or style. The language model may hallucinate information or make up facts that are not consistent with the desired tone or style.

Overall, the key to avoiding hallucination in language models is to provide them with clear and accurate information and context, and to carefully monitor their responses to ensure that they are consistent with your expectations and requirements.