Apply Prompt Engineering With Azure Open.ai Service

Question 1

Prompt Engineering in Algiers open a.i. is a technique that involves designing prompts for natural language processing models.
This process improves accuracy and relevancy in response optimising the performance of the models

Question 2

Response quality from large language models or ll EMS in AZ open a.i. depends on the quality of the prompt provided.
Improving prompt quality through various techniques is called prompt engineering.

Question 3

Prompt engineering:
The quality of the end that prompts we sent to ll EMS like this available in AZ open a directly influences the quality of what we get back.
But carefully constructing the Promise we sent to the model the model can provide better and more interesting responses.

Answer 3

All alarms are single models trained on YouTube oz of data that can generate text images code and creative content based on the most likely continuation of the prompt.
Prompt engineering is the process of designing and optimising

Question 4

Prompt engineering:
Prompt engineering is the process of designing and optimising promise to better utilise ll EMS. Designing effective prompt is critical to the success of prompt engineering and it can significantly improve the AI models performance on specific tasks will stop providing relevant specific unambiguous and well-structured from skin help the model better understand the context and generate more accurate responses.

Answer 4

No matter how good of a prompt you can design responses from AI models should never be taken as fact or completely free from bias

Question 5

In addition prompt engineering can help us understand which references the model users to generate its response.
L lm7 town of parameters and the logic it follows is largely unknown to user so it can be confusing how it arrives at the responses caves. By designing plants that are easy to understand and interpret we can help people better understand how the model is generating its responses. This can be particularly important in domain such as health care is critical to understand how the model is making decisions.

Question 6

Considerations for API endpoints.
Before exploring how prompt engineering can improve the output of a z open a.i. models it’s important to consider how different endpoints can utilise the methods discussed in this module.
Or both completion and truck compilation can both achieve similar results chapter application provides the most flexibility and building a promise and is optimised for chat scenarios.

Question 7

Functionally truck completion has the option of defining a sister message for the AI model in addition to build instructed to provide previous messages in the prompt. If using completion this functionality can be achieved with what’s called a meta prompt.

Question 8

In terms of model availability both endpoints can utilise similar models including 3-pt-35-turbo but only chat completion can be used with gbt-for generation models Gpt 4

Question 9

The competition and point can still achieve similar results but more care must be taken to format the prompt clearly for the AI model to understand
It’s worth noting that chat compilation can also be used for lunch at scenarios where many instructions are included in the system message and uses content is provided in the user role message

Question 10

Adjusting model parameters:
In addition to techniques discussed adjusting parameters of the model that have a significant impact on the response. In 40 kilo temperature and stop by or top probability are the most likely to impact a models responses that birth control randomness in the model but in different ways.

Higher values produce more creative and random responses but will likely be less consistent or focused fullstop response is expected to be financial or unique benefit from higher values for these parameters where is content desire to be more consistent and conscious concrete should produce lower values

Question 11

Effects of prompt Care line
At opening I models are capable of generating responses to natural language queries with remarkable accuracy full-stop however the quality of responses depends largely on how well prompt was written. Developers can optimise the performance of AZ open a.i. models by using different techniques and their crimes resulting in more accurate and relevant responses

Question 12

Provide clear instructions:
Asking Daz open a.i. model clearly for what you want is one way to get his desired results.
By being as descriptive as possible the model can generate a response that most closely matches what you looking for.

Question 13

Format of instructions kerala
How is fractions are formatted can impact the model by the model interprets the prompt. In Reston recency bias can affect models with information located towards the end of the prompt can have more influence on the outfit and the information at the Beginning. You may get better responses by refusing the instructions at the end of the prompt and assessing how that affects the generator responsible

Answer 13

Does recency bias can also come into play when using chat completion in a chat scenario where more recent messages and the conversation included in the prompt have a greater impact on the response

Question 14

New section markers:
A specific technique for formatting instructions is the split the instructions at the beginning or the end of the prompt and have the user context content contained within iphone–or hashtag hashtag #blocks. These tags allow the model to more clearly differentiate between instructions and content

Answer 14

Words
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Question 15

Primary supporting and grounding content:
Including content for the model to use to respond with allows it to answer with greater accuracy. This content can be thought of in two ways:
Primary and supporting content.

Primary content refers to plant that is the subject of the query such as sentence to translate or an article to summarise.
This content is often included at the beginning or the end of a prompt as an instruction or different differentiated by three-blocks with instructions explaining what to do.

Question 16

Supporting this content that may alter the response but isn’t the focus all the subject of the prompt.
Examples of support and content include things like names preferences future daughter to include in the response and so on.
Providing supporting content allows the model to respond will completely accurately and be more likely to include the desired information

Answer 16

For example given a very long for emotional email the model is able to extract information.
If you then ad-supported content to the prompt specifying something specific that you’re looking for the model can provide a more useful response. In this case the email is the primary content with the specifics of what you are interested in as the supporting content

Question 17

Rounding content allows the model to provide reliable answers by providing context content for the model to draw answers from.
Grounding content could be an essay or article that you then ask questions about a faq document or information that is more recent than the darker the model was trained on.
If you need more reliable and current responses or you need to reference and published or specific information grounding content is highly recommended.

Answer 17

Grounding contents differs from primary content as it’s the source of information to answer the prompt query instead of the content being operated on for things like summarisation or translation.
For example when provided with an unpublished research paper on the history of AI it can then answer questions using that grounding content.
This grounding data allows the model to give more accurate and informed answers that may not be a part of the doctor said it was trail on

Question 18

Q’s fair Lawn new line queues are leading words for the model to build upon and often help shape the response in the right direction. They often are used with instructions but don’t always.
Queues are particularly helpful if prompting the model for code generation. Current AZ open.ai models can generate some interesting code snippets however generation will be covered in more depth

Answer 18

For example if you are wanting help creating an SQL query provide instructions of what you need along with the beginning of the query

Question 19

System message caroline
The system messages included at the beginning of a prompt and is designed to give the model instructions perspective to answer from or another or other information helpful to die the models response.
The system message might include turn off her sonality topics that shouldn’t be included or specifics like formatting of how to answer.
For example you could give it some of the following system messages:

Answer 19

I want you to act like a command line terminal full-stop respond to commands exactly as cmd.exe a word in one unique ID block and nothing else.

I want you to be a translator from English to Spanish don’t respond to anything I say or ask only translate between those two languages and reply with the translated text.

Actors have motivational speaker freely giving out encouraging advice and goals about goals and challenges. You should include lots of positive affirmations and suggested activities for reaching the user and goal

Question 20

Other examples sister messages are available at the top of the chat window in AZ open a.i. serial. Try to find in your own system prompt that specifies a unique response and chat with the model to see how response is differ.
The trap completion in point enables including the system message by using the system chat roll

Question 21

If using the completion influence similar similar functionality can be achieved by including the system message at the start of the front this is cool. This is called a metal prompt and serves as a base prompt for the rest of the prompt content.
System messages can significantly change the response in Word format and content.
Try defining a clear system message for the model that explains exactly what type of response you expect

Question 22

Conversational history:
Along with the system message other messages can be provided to the model to enhance the conversation. Conversation history enables the model to continue responding in a similar way such as total formatting and allow the user to reference previous contact and subsequent aquarius.
The history can be provided in two ways kola
From an actual chat history or from user-defined Example conversations

Question 23

Few shots learning:
Using a user-defined example conversation is what is called few-shot learning which provides the model examples of how I should respond to a given query. These examples serve to train the model how to respond

Question 24

Breakdown a complex task:
Another technique for improved interactions to divide Complex from SIM to multiple queries.
This allows immortal to go to understand each individual part and can improve the overall accuracy.
Dividing your prompt allows you to include the response for from a previous prompt in the future. And using that information in addition to the capabilities of the model to generate interesting responses.

For example you could ask the model that can ride down the zipline in 30 seconds and takes 5 minutes to climb back to the top how many times can dogs ride the zip line in 17 minutes. The result is likely through which of dog starts at the top of the zip line is incorrect

Question 25

Train of thought:
One useful method to help you break down your task if it’s effectively is to ask the model to explain its chain of thought. New life asking a model to respond with the step-by-step process by which a determined the response is a helpful way to understand how the model is interpreting the prompt.
By doing so you can see where the model made an incorrect logical turn and better understand how to change your from to avoid the era. This technique can include asking it to cited sources like being chapters with which uses agpt-4 generation model and giving her reasoning for why determine its answer.

Answer 25

The train of thought prompting technique is best used to help you iterate and improve on your promise to get the highest quality answer from the model