Prompt Engineering for Developers.

After the whole ChatGPT thing, we noticed a division amongst the users. Some believed that LLMs are good enough to take people’s job while others thought it has a long way to go.

Well, I have learned that LLMs are a tool to perform tasks and the effectiveness, or their accuracy lies in the hands of the person asking them to perform these tasks. This is nothing but prompt engineering where a person effectively asks the model to execute certain tasks. The response of the model is directly affected by the prompts that it receives. An inaccurate or confused prompt will lead to an inaccurate response.

With these series of articles on prompt engineering, I am going to share some of the best practices for prompting that will help developers in quickly building software applications by leveraging the power of LLM APIs.

We will cover (with code) the following topics.

1. Prompting best practices for software development

2. Some common use cases such as

a. Summarizing

b. Inferring

c. Transforming

d. Expanding

3. Building a chatbot using an LLM

Note — these best practices are focused on Instruction tuned LLMs, such as GPT — 3.5 turbo

Before we start — This series of articles on prompt engineering is compiled from a recently launched course by Andrew Ng and Isa Fulford, you can find all the resources and the course here.

Let’s have a look at the setup.

Setup

!pip install openai
!pip install python-dotenv
import openai
import os

openai.api_key  = 'YOUR_OPENAI_API_KEY'

Get your Open AI API key from here.

# Function to query the model and generate response aginst a prompt
def get_completion(prompt, model="gpt-3.5-turbo"): #chatgpt model = gpt-3.5-turbo
    messages = [{"role": "user", "content": prompt}]
    response = openai.ChatCompletion.create(
        model=model,
        messages=messages,
        temperature=0, # this is the degree of randomness of the model's output
    )
    return response.choices[0].message["content"]

Let’s Begin!!

In the first part, let’s discuss the two basic principles, we will be using these throughout the series.

Principle 1. Write clear and specific instructions.

You should express what you want a model to do by providing instructions that are as clear and specific as you can possibly make them. This will guide the model towards the desired output and reduce the chance that you get irrelevant or incorrect responses. Don’t confuse writing a clear prompt with writing a short prompt, because in many cases, longer prompts actually provide more clarity and context for the model, which can actually lead to more detailed and relevant outputs.

Below are some of the tactics which will help you put this principle in action.

1. Use delimiters to clearly indicate distinct parts of the input. These could be triple backticks ```, triple quotes “””, XML tags <tag> </tags>, angle brackets < >, etc. anything that makes this clear to the model that this is a separate section. Using delimiters is also a helpful technique to try and avoid prompt injections. Prompt injection is, if a user is allowed to add some input into your prompt, they might give conflicting instructions to the model that might make it follow the user’s instructions rather than doing what you want it to do.

text = f"""
You should express what you want a model to do by \ 
providing instructions that are as clear and \ 
specific as you can possibly make them. \ 
This will guide the model towards the desired output, \ 
and reduce the chances of receiving irrelevant \ 
or incorrect responses. Don't confuse writing a \ 
clear prompt with writing a short prompt. \ 
In many cases, longer prompts provide more clarity \ 
and context for the model, which can lead to \ 
more detailed and relevant outputs.
"""
prompt = f"""
Summarize the text delimited by triple backticks \ 
into a single sentence.
```{text}``` 
"""
#using ''' for text insertion. 
# Summarize the instructions in triple backticks, instead of following them. 
response = get_completion(prompt)
print(response)

Output

To guide a model towards the desired output and reduce the chances of 
irrelevant or incorrect responses, it is important to provide clear and 
specific instructions, which may require longer prompts for more clarity 
and context.

2. Ask for a structured output.

To make parsing the model outputs easier, it can be helpful to ask for a structured output like HTML or JSON. This output can be directly read into dictionary or list using python.

prompt = f"""
Generate a list of three made-up book titles along \ 
with their authors and genres. 
Provide them in JSON format with the following keys: 
book_id, title, author, genre.
"""
response = get_completion(prompt)
print(response)

Output

[
  {
    "book_id": 1,
    "title": "The Lost City of Zorath",
    "author": "Aria Blackwood",
    "genre": "Fantasy"
  },
  {
    "book_id": 2,
    "title": "The Last Survivors",
    "author": "Ethan Stone",
    "genre": "Science Fiction"
  },
  {
    "book_id": 3,
    "title": "The Secret of the Haunted Mansion",
    "author": "Lila Rose",
    "genre": "Mystery"
  }
]

3. Ask the model to check whether conditions are satisfied. Check assumptions required to do the task.

In this example, we will ask the model to check if the text provided has a set of instructions or not. if it contains instructions then we are going to ask the model to rewrite these instructions in more readable fashion.

The first text contains instructions of making a tea.

text_1 = f"""
Making a cup of tea is easy! First, you need to get some \ 
water boiling. While that's happening, \ 
grab a cup and put a tea bag in it. Once the water is \ 
hot enough, just pour it over the tea bag. \ 
Let it sit for a bit so the tea can steep. After a \ 
few minutes, take out the tea bag. If you \ 
like, you can add some sugar or milk to taste. \ 
And that's it! You've got yourself a delicious \ 
cup of tea to enjoy.
"""
prompt = f"""
You will be provided with text delimited by triple quotes. 
If it contains a sequence of instructions, \ 
re-write those instructions in the following format:

Step 1 - ...
Step 2 - …
…
Step N - …

If the text does not contain a sequence of instructions, \ 
then simply write \"No steps provided.\"

\"\"\"{text_1}\"\"\"
"""
response = get_completion(prompt)
print("Completion for Text 1:")
print(response)Making a cup of tea is easy! First, you need to get some \ 

Output

Completion for Text 1:
Step 1 - Get some water boiling.
Step 2 - Grab a cup and put a tea bag in it.
Step 3 - Once the water is hot enough, pour it over the tea bag.
Step 4 - Let it sit for a bit so the tea can steep.
Step 5 - After a few minutes, take out the tea bag.
Step 6 - Add some sugar or milk to taste.
Step 7 - Enjoy your delicious cup of tea!

The second piece of text describes a sunny day and has no instructions.

text_2 = f"""
The sun is shining brightly today, and the birds are \
singing. It's a beautiful day to go for a \ 
walk in the park. The flowers are blooming, and the \ 
trees are swaying gently in the breeze. People \ 
are out and about, enjoying the lovely weather. \ 
Some are having picnics, while others are playing \ 
games or simply relaxing on the grass. It's a \ 
perfect day to spend time outdoors and appreciate the \ 
beauty of nature.
"""
prompt = f"""
You will be provided with text delimited by triple quotes. 
If it contains a sequence of instructions, \ 
re-write those instructions in the following format:

Step 1 - ...
Step 2 - …
…
Step N - …

If the text does not contain a sequence of instructions, \ 
then simply write \"No steps provided.\"

\"\"\"{text_2}\"\"\"
"""
response = get_completion(prompt)
print("Completion for Text 2:")
print(response)

Output

Completion for Text 2:
No steps provided.

The model determined that there were no instructions in the text.

4. Few-shot prompting — Give successful examples of completing tasks then ask model to perform the task.

By this tactic, we’re telling the model that its task is to answer in a consistent style. The style used here is a conversation between a child and a grandparent where the child is asks a question and the grandparent answers with metaphors.

Next we feed this conversation to the model and see how the model replicates the style of the grandparent to answer the next question.

prompt = f"""
Your task is to answer in a consistent style.

<child>: Teach me about patience.

<grandparent>: The river that carves the deepest \ 
valley flows from a modest spring; the \ 
grandest symphony originates from a single note; \ 
the most intricate tapestry begins with a solitary thread.

<child>: Teach me about resilience.
"""
response = get_completion(prompt)
print(response)

Output

<grandparent>: Resilience is like a tree that bends with the wind but never 
breaks. It is the ability to bounce back from adversity and keep moving 
forward, even when things get tough. Just like a tree that grows stronger 
with each storm it weathers, resilience is a quality that can be developed 
and strengthened over time.

These are some simple examples of how we can give the model a clear and specific instruction. Now, let’s move on to our second principle

Principle 2. Give the model time to think.

Generally, if a model is making reasoning errors by rushing to an incorrect conclusion, you should try reframing the query to request a chain or series of relevant reasoning before the model provides its final answer.

In other words, if you give a model a task that’s too complex for it to do in a short amount of time or in a small number of words, it may make up a guess which is likely to be incorrect.

The same thing would happen with a person too, if they are asked to solve a complex problem in a short amount of time, chances are, they will make mistakes.

To solve such issues and get the maximum outcome from a model’s intelligence, you can instruct the model to think longer about a problem which means it will spend more computational effort on the task to reach the right conclusion/answer.

So, let’s see some tactics and examples around our second principle.

1. Specify the steps required to complete a task.

In this example, we have used a text and we are going to specify multiple steps that the model will follow on the text.

text = f"""
In a charming village, siblings Jack and Jill set out on \ 
a quest to fetch water from a hilltop \ 
well. As they climbed, singing joyfully, misfortune \ 
struck—Jack tripped on a stone and tumbled \ 
down the hill, with Jill following suit. \ 
Though slightly battered, the pair returned home to \ 
comforting embraces. Despite the mishap, \ 
their adventurous spirits remained undimmed, and they \ 
continued exploring with delight.
"""
# example 1
prompt_1 = f"""
Perform the following actions: 
1 - Summarize the following text delimited by triple \
backticks with 1 sentence.
2 - Translate the summary into French.
3 - List each name in the French summary.
4 - Output a json object that contains the following \
keys: french_summary, num_names.

Separate your answers with line breaks.

Text:
```{text}```
"""
response = get_completion(prompt_1)
print("Completion for prompt 1:")
print(response)

Output

Completion for prompt 1:
Two siblings, Jack and Jill, go on a quest to fetch water from a well on a 
hilltop, but misfortune strikes and they both tumble down the hill, returning 
home slightly battered but with their adventurous spirits undimmed.

Deux frères et sœurs, Jack et Jill, partent en quête d'eau d'un puits sur une 
colline, mais un malheur frappe et ils tombent tous les deux de la colline, 
rentrant chez eux légèrement meurtris mais avec leurs esprits aventureux 
intacts.
 Noms: Jack, Jill. 

{
  "french_summary": "Deux frères et sœurs, Jack et Jill, partent en quête 
d'eau d'un puits sur une colline, mais un malheur frappe et ils tombent tous 
les deux de la colline, rentrant chez eux légèrement meurtris mais avec 
leurs esprits aventureux intacts.",
  "num_names": 2
}

Next, we may use earlier tactic and ask for the output to be in a specified format on the same text.

prompt_2 = f"""
Your task is to perform the following actions: 
1 - Summarize the following text delimited by 
  <> with 1 sentence.
2 - Translate the summary into French.
3 - List each name in the French summary.
4 - Output a json object that contains the 
  following keys: french_summary, num_names.

Use the following format:
Text: <text to summarize>
Summary: <summary>
Translation: <summary translation>
Names: <list of names in Italian summary>
Output JSON: <json with summary and num_names>

Text: <{text}>
"""
response = get_completion(prompt_2)
print("\nCompletion for prompt 2:")
print(response)

Output

Completion for prompt 2:
Summary: Jack and Jill go on a quest to fetch water, but misfortune strikes 
and they tumble down the hill, returning home slightly battered but with their 
adventurous spirits undimmed. 
Translation: Jack et Jill partent en quête d'eau, mais la malchance frappe et 
ils dégringolent la colline, rentrant chez eux légèrement meurtris mais avec 
leurs esprits aventureux intacts.
Names: Jack, Jill
Output JSON: {"french_summary": "Jack et Jill partent en quête d'eau, mais la 
malchance frappe et ils dégringolent la colline, rentrant chez eux légèrement 
meurtris mais avec leurs esprits aventureux intacts.", "num_names": 2}

2. Instruct the model to work out its own solution before rushing to a conclusion.

Let’s ask our model to evaluate a math solution, if it is correct or not.

The below prompt is a math question followed by a solution and the model is directly asked (in the beginning) to determine if the solution is correct or not.

prompt = f"""
Determine if the student's solution is correct or not.

Question:
I'm building a solar power installation and I need \
 help working out the financials. 
- Land costs $100 / square foot
- I can buy solar panels for $250 / square foot
- I negotiated a contract for maintenance that will cost \ 
me a flat $100k per year, and an additional $10 / square \
foot
What is the total cost for the first year of operations 
as a function of the number of square feet.

Student's Solution:
Let x be the size of the installation in square feet.
Costs:
1. Land cost: 100x
2. Solar panel cost: 250x
3. Maintenance cost: 100,000 + 100x
Total cost: 100x + 250x + 100,000 + 100x = 450x + 100,000
"""
response = get_completion(prompt)
print(response)

Output

The student's solution is correct.

Please solve the problem and you’ll find that the student’s solution is actually not correct and to fix this, we will instruct the model to work out its own solution first and then compare the two results. Note that, when we are instructing the model, we are actually giving it the necessary time to think through the solution.

Below is the updated prompt.

prompt = f"""
Your task is to determine if the student's solution \
is correct or not.
To solve the problem do the following:
- First, work out your own solution to the problem. 
- Then compare your solution to the student's solution \ 
and evaluate if the student's solution is correct or not. 
Don't decide if the student's solution is correct until 
you have done the problem yourself.

Use the following format:
Question:
```
question here
```
Student's solution:
```
student's solution here
```
Actual solution:
```
steps to work out the solution and your solution here
```
Is the student's solution the same as actual solution \
just calculated:
```
yes or no
```
Student grade:
```
correct or incorrect
```

Question:
```
I'm building a solar power installation and I need help \
working out the financials. 
- Land costs $100 / square foot
- I can buy solar panels for $250 / square foot
- I negotiated a contract for maintenance that will cost \
me a flat $100k per year, and an additional $10 / square \
foot
What is the total cost for the first year of operations \
as a function of the number of square feet.
``` 
Student's solution:
```
Let x be the size of the installation in square feet.
Costs:
1. Land cost: 100x
2. Solar panel cost: 250x
3. Maintenance cost: 100,000 + 100x
Total cost: 100x + 250x + 100,000 + 100x = 450x + 100,000
```
Actual solution:
"""
response = get_completion(prompt)
print(response)

Output

Let x be the size of the installation in square feet.

Costs:
1. Land cost: 100x
2. Solar panel cost: 250x
3. Maintenance cost: 100,000 + 10x

Total cost: 100x + 250x + 100,000 + 10x = 360x + 100,000

Is the student's solution the same as actual solution just calculated:
No

Student grade:
Incorrect

As we give the model enough time to think with specific instructions, the model successfully evaluates the solution and identifies the student’s answer as correct or incorrect.

Bonus principle — Model Limitations: Hallucinations Boie is a real company, the product name is not real.

While developing applications based on Large Language Models, one should always know the limitations of these models. If the model is being exposed to a vast amount of knowledge during its training process, it has not perfectly memorized the information it’s seen, and hence it doesn’t know the boundary of its knowledge very well. This means that the model might try to answer questions about obscure topics and can make things up that sound plausible but are not actually true and these fabricated ideas are called as Hallucinations.

Let’s see with the help of an example of a case where the model will hallucinate something. This is an example of where the model confabulates a description of a made-up product name from a real toothbrush company.

When we give the below prompt as input to our model, it gives us a realistic sounding description of a fictitious product.

prompt = f"""
Tell me about AeroGlide UltraSlim Smart Toothbrush by Boie
"""
response = get_completion(prompt)
print(response)

Output

The AeroGlide UltraSlim Smart Toothbrush by Boie is a high-tech toothbrush that
uses advanced sonic technology to provide a deep and thorough clean. It 
features a slim and sleek design that makes it easy to hold and maneuver, and 
it comes with a range of smart features that help you optimize your 
brushing routine.

One of the key features of the AeroGlide UltraSlim Smart Toothbrush is its 
advanced sonic technology, which uses high-frequency vibrations to break up 
plaque and bacteria on your teeth and gums. This technology is highly effective
at removing even the toughest stains and buildup, leaving your teeth feeling 
clean and refreshed.

In addition to its sonic technology, the AeroGlide UltraSlim Smart Toothbrush 
also comes with a range of smart features that help you optimize your brushing 
routine. These include a built-in timer that ensures you brush for the 
recommended two minutes, as well as a pressure sensor that alerts you if you're
brushing too hard.

Overall, the AeroGlide UltraSlim Smart Toothbrush by Boie is a highly advanced 
and effective toothbrush that is perfect for anyone looking to take their oral 
hygiene to the next level. With its advanced sonic technology and smart 
features, it provides a deep and thorough clean that leaves your teeth feeling
fresh and healthy.

this is quite dangerous as the output generated looks very much realistic while it is completely false information.

So, make sure to use the techniques that we’ve gone through in this blog to try to avoid this when you’re building your own applications. This is a known weakness of LLMs and engineers are actively working on combating.

Reducing Hallucinations — First find relevant information, then answer the question based on the relevant information.

In the case that you want the model to generate answers based on a text, first ask the model to find any relevant quotes from the text, then ask it to use those quotes to answer questions and have a way to trace the answer back to the source document.

This tactic is often pretty helpful to reduce these hallucinations.

That’s it, we are done with the basic guidelines, principles and tactics for prompt engineering, in the next article, we will see some common use cases such as summarizing and inferring.