Prompt engineering techniques and best practices: Learn by doing with Anthropic’s Claude 3 on Amazon Bedrock

You have likely already had the opportunity to interact with generative artificial intelligence (AI) tools (such as virtual assistants and chatbot applications) and noticed that you don’t always get the answer you are looking for, and that achieving it may not be straightforward. Large language models (LLMs), the models behind the generative AI revolution, receive instructions on what to do, how to do it, and a set of expectations for their response by means of a natural language text called a prompt. The way prompts are crafted greatly impacts the results generated by the LLM. Poorly written prompts will often lead to hallucinations, sub-optimal results, and overall poor quality of the generated response, whereas good-quality prompts will steer the output of the LLM to the output we want.

In this post, we show how to build efficient prompts for your applications. We use the simplicity of Amazon Bedrock playgrounds and the state-of-the-art Anthropic’s Claude 3 family of models to demonstrate how you can build efficient prompts by applying simple techniques.

Prompt engineering

Prompt engineering is the process of carefully designing the prompts or instructions given to generative AI models to produce the desired outputs. Prompts act as guides that provide context and set expectations for the AI. With well-engineered prompts, developers can take advantage of LLMs to generate high-quality, relevant outputs. For instance, we use the following prompt to generate an image with the Amazon Titan Image Generation model:

An illustration of a person talking to a robot. The person looks visibly confused because he can not instruct the robot to do what he wants.

We get the following generated image.

Let’s look at another example. All the examples in this post are run using Claude 3 Haiku in an Amazon Bedrock playground. Although the prompts can be run using any LLM, we discuss best practices for the Claude 3 family of models. In order to get access to the Claude 3 Haiku LLM on Amazon Bedrock, refer to Model access.

We use the following prompt:

What is 10 + 10?

Claude 3 Haiku’s response:

10 + 10 is 20

The request prompt is actually very ambiguous. 10 + 10 may have several valid answers; in this case, Claude 3 Haiku, using its internal knowledge, determined that 10 + 10 is 20. Let’s change the prompt to get a different answer for the same question:

1 + 1 is an addition
1 - 1 is a substraction
1 * 1 is multiplication
1 / 1 is a division

What is 10 + 10?

Claude 3 Haiku’s response:

10 + 10 is an addition. The answer is 20.

The response changed accordingly by specifying that 10 + 10 is an addition. Additionally, although we didn’t request it, the model also provided the result of the operation. Let’s see how, through a very simple prompting technique, we can obtain an even more succinct result:

1 + 1 is an addition
1 - 1 is a substraction
1 * 1 is multiplication
1 / 1 is a division

What is 10 + 10?

Answer only as in the examples provided and 
provide no additional information.

Claude 3 Haiku response:

10 + 10 is an addition.

Well-designed prompts can improve user experience by making AI responses more coherent, accurate, and useful, thereby making generative AI applications more efficient and effective.

The Claude 3 model family

The Claude 3 family is a set of LLMs developed by Anthropic. These models are built upon the latest advancements in natural language processing (NLP) and machine learning (ML), allowing them to understand and generate human-like text with remarkable fluency and coherence. The family is comprised of three models: Haiku, Sonnet, and Opus.

Haiku is the fastest and most cost-effective model on the market. It is a fast, compact model for near-instant responsiveness. For the vast majority of workloads, Sonnet is two times faster than Claude 2 and Claude 2.1, with higher levels of intelligence, and it strikes the ideal balance between intelligence and speed—qualities especially critical for enterprise use cases. Opus is the most advanced, capable, state-of-the-art foundation model (FM) with deep reasoning, advanced math, and coding abilities, with top-level performance on highly complex tasks.

Among the key features of the model’s family are:

• Vision capabilities – Claude 3 models have been trained to not only understand text but also images, charts, diagrams, and more.
• Best-in-class benchmarks – Claude 3 exceeds existing models on standardized evaluations such as math problems, programming exercises, and scientific reasoning. Specifically, Opus outperforms its peers on most of the common evaluation benchmarks for AI systems, including undergraduate level expert knowledge (MMLU), graduate level expert reasoning (GPQA), basic mathematics (GSM8K), and more. It exhibits high levels of comprehension and fluency on complex tasks, leading the frontier of general intelligence.
• Reduced hallucination – Claude 3 models mitigate hallucination through constitutional AI techniques that provide transparency into the model’s reasoning, as well as improved accuracy. Claude 3 Opus shows an estimated twofold gain in accuracy over Claude 2.1 on difficult open-ended questions, reducing the likelihood of faulty responses.
• Long context window – Claude 3 models excel at real-world retrieval tasks with a 200,000-token context window, the equivalent of 500 pages of information.

To learn more about the Claude 3 family, see Unlocking Innovation: AWS and Anthropic push the boundaries of generative AI together, Anthropic’s Claude 3 Sonnet foundation model is now available in Amazon Bedrock, and Anthropic’s Claude 3 Haiku model is now available on Amazon Bedrock.

The anatomy of a prompt

As prompts become more complex, it’s important to identify its various parts. In this section, we present the components that make up a prompt and the recommended order in which they should appear:

1. Task context: Assign the LLM a role or persona and broadly define the task it is expected to perform.
2. Tone context: Set a tone for the conversation in this section.
3. Background data (documents and images): Also known as context. Use this section to provide all the necessary information for the LLM to complete its task.
4. Detailed task description and rules: Provide detailed rules about the LLM’s interaction with its users.
5. Examples: Provide examples of the task resolution for the LLM to learn from them.
6. Conversation history: Provide any past interactions between the user and the LLM, if any.
7. Immediate task description or request: Describe the specific task to fulfill within the LLMs assigned roles and tasks.
8. Think step-by-step: If necessary, ask the LLM to take some time to think or think step by step.
9. Output formatting: Provide any details about the format of the output.
10. Prefilled response: If necessary, prefill the LLMs response to make it more succinct.

The following is an example of a prompt that incorporates all the aforementioned elements:

Human: You are a solutions architect working at Amazon Web Services (AWS) 
 named John Doe. 

Your goal is to answer customers' questions regarding AWS best architectural 
 practices and principles. 
Customers may be confused if you don't respond in the character of John.

You should maintain a friendly customer service tone.

Answer the customers' questions using the information provided below

<context>{{CONTEXT}}</context>

Here are some important rules for the interaction:

- Always stay in character, as John, a solutions architect that 
 work at AWS. 

- If you are unsure how to respond, say "Sorry, I didn't understand that. 
 Could you repeat the question?"

- If someone asks something irrelevant, say, "Sorry, I am John and I give AWS 
 architectural advise. Do you have an AWS architecture question today I can 
 help you with?" 
Here is an example of how to respond in a standard interaction: 
 <example> 
 User: Hi, what do you do? 
 John: Hello! My name is John, and I can answer your questions about best 
 architectural practices on AWS. What can I help you with today?
 </example> 


Here is the conversation history (between the user and you) prior to the 
 question. It could be empty if there is no history: 
 <history>{{HISTORY}}</history> 


Here is the user's question: <question>{{QUESTION}}</question> 
 How do you respond to the user's question? 


Think about your answer first before you respond.

Put your response in <response></responses>

Assistant: <response>

Best prompting practices with Claude 3

In the following sections, we dive deep into Claude 3 best practices for prompt engineering.

Text-only prompts

For prompts that deal only with text, follow this set of best practices to achieve better results:

• Mark parts of the prompt with XLM tags – Claude has been fine-tuned to pay special attention to XML tags. You can take advantage of this characteristic to clearly separate sections of the prompt (instructions, context, examples, and so on). You can use any names you prefer for these tags; the main idea is to delineate in a clear way the content of your prompt. Make sure you include <> and </> for the tags.
• Always provide good task descriptions – Claude responds well to clear, direct, and detailed instructions. When you give an instruction that can be interpreted in different ways, make sure that you explain to Claude what exactly you mean.
• Help Claude learn by example – One way to enhance Claude’s performance is by providing examples. Examples serve as demonstrations that allow Claude to learn patterns and generalize appropriate behaviors, much like how humans learn by observation and imitation. Well-crafted examples significantly improve accuracy by clarifying exactly what is expected, increase consistency by providing a template to follow, and boost performance on complex or nuanced tasks. To maximize effectiveness, examples should be relevant, diverse, clear, and provided in sufficient quantity (start with three to five examples and experiment based on your use case).
• Keep the responses aligned to your desired format – To get Claude to produce output in the format you want, give clear directions, telling it exactly what format to use (like JSON, XML, or markdown).
• Prefill Claude’s response – Claude tends to be chatty in its answers, and might add some extra sentences at the beginning of the answer despite being instructed in the prompt to respond with a specific format. To improve this behavior, you can use the assistant message to provide the beginning of the output.
• Always define a persona to set the tone of the response – The responses given by Claude can vary greatly depending on which persona is provided as context for the model. Setting a persona helps Claude set the proper tone and vocabulary that will be used to provide a response to the user. The persona guides how the model will communicate and respond, making the conversation more realistic and tuned to a particular personality. This is especially important when using Claude as the AI behind a chat interface.
• Give Claude time to think – As recommended by Anthropic’s research team, giving Claude time to think through its response before producing the final answer leads to better performance. The simplest way to encourage this is to include the phrase “Think step by step” in your prompt. You can also capture Claude’s step-by-step thought process by instructing it to “please think about it step-by-step within <thinking></thinking> tags.”
• Break a complex task into subtasks – When dealing with complex tasks, it’s a good idea to break them down and use prompt chaining with LLMs like Claude. Prompt chaining involves using the output from one prompt as the input for the next, guiding Claude through a series of smaller, more manageable tasks. This improves accuracy and consistency for each step, makes troubleshooting less complicated, and makes sure Claude can fully focus on one subtask at a time. To implement prompt chaining, identify the distinct steps or subtasks in your complex process, create separate prompts for each, and feed the output of one prompt into the next.
• Take advantage of the long context window – Working with long documents and large amounts of text can be challenging, but Claude’s extended context window of over 200,000 tokens enables it to handle complex tasks that require processing extensive information. This feature is particularly useful with Claude Haiku because it can help provide high-quality responses with a cost-effective model. To take full advantage of this capability, it’s important to structure your prompts effectively.
• Allow Claude to say “I don’t know” – By explicitly giving Claude permission to acknowledge when it’s unsure or lacks sufficient information, it’s less likely to generate inaccurate responses. This can be achieved by adding a preface to the prompt, such as, “If you are unsure or don’t have enough information to provide a confident answer, simply say ‘I don’t know’ or ‘I’m not sure.'”

Prompts with images

The Claude 3 family offers vision capabilities that can process images and return text outputs. It’s capable of analyzing and understanding charts, graphs, technical diagrams, reports, and other visual assets. The following are best practices when working with images with Claude 3:

• Image placement and size matters – For optimal performance, when working with Claude 3’s vision capabilities, the ideal placement for images is at the very start of the prompt. Anthropic also recommends resizing an image before uploading and striking a balance between image clarity and image size. For more information, refer to Anthropic’s guidance on image sizing.
• Apply traditional techniques – When working with images, you can apply the same techniques used for text-only prompts (such as giving Claude time to think or defining a role) to help Claude improve its responses.

Consider the following example, which is an extraction of the picture “a fine gathering” (Author: Ian Kirck, https://en.m.wikipedia.org/wiki/File:A_fine_gathering_(8591897243).jpg).

We ask Claude 3 to count how many birds are in the image:

You are a bird expert who has perfect vision and pay a lot of attention to details. 
How many birds are in this picture? Before providing the number of birds in <answer>
tags, take the time to think in <thinking> tags analyzing each part of the picture 
from left to right.

Claude 3 Haiku’s response:

<thinking>
Alright, let me carefully examine this image as a bird expert with keen visual acuity.
Starting from the left, I see three birds with mottled brown and white plumage, 
which appear to be some type of shorebird like a sandpiper or plover. 
Next, there is a group of four more birds with similar mottled plumage. 
In the center, there is a striking black-headed gull or tern with a white body. 
To the right of the gull, there is another bird with the same distinctive black head and white body. 
Finally, at the far right, I can make out the silhouette of another small bird.
</thinking>

<answer>9 birds</answer>

In this example, we asked Claude to take some time to think and put its
reasoning in an XML tag and the final answer in another. Also, we gave Claude time to think and clear instructions to pay attention to details, which helped Claude to provide the correct response.

• Take advantage of visual prompts – The ability to use images also enables you to add prompts directly within the image itself instead of providing a separate prompt.

Let’s see an example with the following image:

In this case, the image itself is the prompt:
Claude 3 Haiku’s response:

To solve for the missing side length X, we can use the Pythagorean theorem. 
The Pythagorean theorem states that in a right-angled triangle, the square of the 
length of the hypotenuse (the longest side) is equal to the sum of the squares 
of the lengths of the other two sides.
 
Given:
- One side is 12 centimeters.
- The other side is 10 centimeters.

Let's denote the missing side length as X.

Using the Pythagorean theorem:

(12)^2 + (10)^2 = X^2
144 + 100 = X^2
244 = X^2
X = √244
X ≈ 15.63 centimeters

Therefore, the length of the missing side X is approximately 15.63 centimeters.

• Examples are also valid using images – You can provide multiple images in the same prompt and take advantage of Claude’s vision capabilities to provide examples and additional valuable information using the images. Make sure you use image tags to clearly identify the different images. Because this question is a reasoning and mathematical question, set the temperature to 0 for a more deterministic response.

Let’s look at the following example:

Prompt:

You are an expert at logical reasoning and counting figures. When you identify figures 
in a given image, you always pay special attention to all the lines and vertices that can 
form distinct smaller figures within the overall large shape.

Using the following images, image 1 and image 2

<image1>

</image1>

<image2>

</image2>

Please tell me how many triangles do you see in <image1> using as reference <image2> 
to identify the vertices of the triangles.

Here is an example of how to list out the identified triangles using the vertices:

Large triangle: 1 (ABC)

Please take the time to analyze the image in detail in <thinking> tags and put your 
response in <response> tags.

Claude 3 Haiku’s response:

<thinking>

Analyzing the image 1 in the tags <image1>, and using the image 2 in the tags <image2> as 
a reference for identifying the vertices, I can see the following triangles:

1. The large triangle formed by the outer lines (vertices A, B, C)
2. The smaller triangle formed by the lines within the larger triangle (vertices C, D, A)
3. The second smaller triangle formed by the lines within the larger triangle (vertices D, B, C)
4. The third smaller triangle formed by the lines, with vertices A, B, and D

So in total, there are 4 distinct triangles visible in the image 1.
</thinking>

<response>

In the image 1 in the tags <image1>, there are 4 triangles:

1. The large triangle ABC
2. The smaller triangle ACD
3. The second smaller triangle DBC
4. The third smaller triangle ABD

</response>

• Use detailed descriptions when working with complicated charts or graphics – Working with charts or graphics is a relatively straightforward task when using Claude’s models. We simply take advantage of Claude’s vision capabilities, pass the charts or graphics in image format, and then ask questions about the provided images. However, when working with complicated charts that have lots of colors (which look very similar) or a lot of data points, it’s a good practice to help Claude better understand the information with the following methods:
  • Ask Claude to describe in detail each data point that it sees in the image.
  • Ask Claude to first identify the HEX codes of the colors in the graphics to clearly see the difference in colors.

Let’s see an example. We pass to Claude the following map chart in image format (source: https://ourworldindata.org/co2-and-greenhouse-gas-emissions), then we ask about Japan’s greenhouse gas emissions.

Prompt:

What are the greenhouse gas emissions for Japan in 2021? Prior to provide your response, 
please take your time to think in <thinking> tags, identifying the nine color used and 
their corresponding legend using HEX code. Then, provide your response in <response> tags.

Claude 3 Haiku’s response:

<thinking>

Analyzing the distinct colors used in the color map and their corresponding legend:

The color scale on the map uses the following 9 distinct colors:

1. Light yellow: HEX code #FFFFD0, corresponding to "0 t" emissions range
2. Pale orange: HEX code #FFD580, corresponding to "10 million t" emissions range
3. Slightly darker orange: HEX code #FFA852, corresponding to "30 million t" emissions range
4. Bright orange: HEX code #FFA040, corresponding to "100 million t" to "300 million t" emissions range
5. Darker orange: HEX code #FF7F2A, corresponding to "300 million t" to "1 billion t" emissions range
6. Reddish-orange: HEX code #FF6018, corresponding to "1 billion t" to "3 billion t" emissions range
7. Darker reddish-orange: HEX code #E64700, corresponding to "3 billion t" to "10 billion t" emissions range
8. Maroon: HEX code #8B0000, corresponding to "10 billion t" emissions range
9. No data: Shown as a gray color

The color used for Japan is the darker orange shade with HEX code #FF7F2A, which 
corresponds to the "300 million t" to "1 billion t" emissions range.

</thinking>

<response>

Based on the color analysis and the corresponding legend, the greenhouse gas emissions for 
Japan in 2021 are in the range of 300 million to 1 billion tonnes of carbon dioxide-equivalents.

</response>

You are presenting the 2023 sustainable development goals report from OECD 
and you are currently in slide 9.

Please narrate the slide 9 in <image> tags that provides information about the ninth 
goal as if you were the presenter.

Avoid discussing any topics, particularly acronyms, unless you are completely confident 
in your understanding of their meaning and do not introduce information that is not 
explicitly presented on the current slide. Make sure you narrate every single number 
showed in the slide and all the details. If you fail to mention every number in the slide, 
some viewers who are visual impaired won’t be able to read the information by themselves.

Put your narration in <narration> tags.

<narration>

This slide focuses on the 9th Sustainable Development Goal, which is to "Build Resilient 
Infrastructure, Promote Inclusive and Sustainable Industrialization and Foster Innovation".

The slide presents information on global manufacturing. It shows that global manufacturing 
growth slowed from 7.4% in 2021 to 3.3% in 2022, due to factors like inflation, energy 
price shocks, supply chain disruptions, and global economic deceleration.

The slide also highlights that CO2 emissions from energy-related sources reached a record 
high of 36.8 billion metric tons in 2022.

Furthermore, the slide indicates that less developed countries, or LDCs, are likely to miss 
their 2030 target of doubling their manufacturing share of GDP. In 2015, this share was 12.1%, 
rising to 14% in 2022, but the 2030 target is 24.2%.

The regional breakdown shows that sub-Saharan Africa has the lowest manufacturing share at 
21.7%, Europe and North America has the highest at 47.1%, and Eastern Asia is in the middle 
at 47.7%.

</narration>

Human: You are an information extraction system. Your task is to extract key information 
 from the text enclosed between <post></post> and put it in JSON. 


Here are some basic rules for the task: 
 - Do not output your reasoning for the extraction 
 - Always produce complete and valid JSON objects 
 - If no information can be extracted or you can not produce a valid JSON object output 
 an empty json object "{}"

Here are some examples of how to extract information from text: 
 <examples> 
 <example_1> 
 <post>
 """Six months ago, Wall Street Journal reporter Evan Gershkovich was detained in Russia 
 during a reporting trip. He remains in a Moscow prison. We’re offering resources for 
 those who want to show their support for him. #IStandWithEvan https://wsj.com/Evan"""
 </post>
 <json>
 {
 "topic": "detention of a reporter",
 "location": "Moscow"
 "entities": ["Evan Gershkovich", "Wall Street Journal"],
 "keyphrases": ["reporter", "detained", "prison"],
 "sentiment": "negative",
 "links": ["https://wsj.com/Evan"],
 }
 </json>
 </example_1>
 
 <example_2>
 <post>
 """'We’re living an internal war': Once-peaceful Ecuador has become engulfed in the 
 cocaine trade, and the bodies are piling up."""
 </post>
 <json>
 {
 "topic": "drug war",
 "location": "Ecuador",
 "entities": ["Ecuador"],
 "keyphrases": ["drug war", "cocaine trade"],
 "sentiment": "negative",
 "links": [],
 }
 </json>
 </example_2>
 
 </examples>


Extract information from the following post. Generate only a complete JSON object and put 
 it in <json></json>.
 <post>
 """A postpandemic hiring spree has left airports vulnerable to security gaps as new staff 
 gain access to secure areas, creating an opening for criminal groups."""
 </post>


Use the following JSON object definition to write your answer
 <json_definition>
 {
 "type": "object",
 "properties": {
 "topic": {
 "description": "the main topic of the post",
 "type": "string",
 "default": ""
 },
 "location": {
 "description": "the location, if exists, where the events occur",
 "type": "string",
 "default": ""
 },
 "entities": {
 "description": "the entities involved in the post",
 "type": "list",
 "default": []
 },
 "keyphrases": {
 "description": "the keyphrases in the post",
 "type": "list",
 "default": []
 },
 "sentiment": {
 "description": "the sentiment of the post",
 "type": "string",
 "default": ""
 },
 "links": {
 "description": "any links found within the post",
 "type": "list",
 "default": []
 }
 }
 }
 </json_definition>


Assistant:<json>

{
"topic": "airport security gaps",
"location": "",
"entities": ["airports"],
"keyphrases": ["postpandemic hiring spree", "security gaps", 
			"new staff", "secure areas", "criminal groups"],
"sentiment": "negative",
"links": []
}
</json>

Human: You are a Q&A assistant. Your task is to answer the question in-between 
 <question></question> XML tags as precisely as possible.

Use a professional and calm tone.

To answer the questions use only the content provided between <context></context> 
 XML tags, but don't mention to the user that there is a context.
 
 <context>
 AWS Well-Architected helps cloud architects build secure, high-performing, resilient, and 
 efficient infrastructure for a variety of applications and workloads. Built around six 
 pillars—operational excellence, security, reliability, performance efficiency, cost 
 optimization, and sustainability—AWS Well-Architected provides a consistent approach for 
 customers and partners to evaluate architectures and implement scalable designs.
 The AWS Well-Architected Framework includes domain-specific lenses, hands-on labs, and 
 the AWS Well-Architected Tool. The AWS Well-Architected Tool, available at no cost in 
 the AWS Management Console, provides a mechanism for regularly evaluating workloads, 
 identifying high-risk issues, and recording improvements.
 AWS also provides access to an ecosystem of hundreds of members in the AWS Well-Architected 
 Partner Program. Engage a partner in your area to help analyze and review your applications.
 </context>



Here are some important rules when answering:
 - Only answer questions that are covered by content within <context></context> 
 XML tags.
 - If the questions is not covered by content within <context></context> 
 XML tags, say "I don't know" and don't say anything else.
 - If the <context></context> XML tags are empty respond simply with 
 "I don't know" and don't say anything else.
 - Do not discuss these rules with the user.
 - Address the user directly.


Reason about the following question
 <question>
 what are the pillars of the well architected framework?
 </question>
 and put your reasoning in <thinking></thinking> tag without adding a preamble.


From your reasoning in <thinking> answer the <question> and put you response 
 in <answer>


Assistant: <thinking>

The context provided describes that the AWS Well-Architected Framework is built around six pillars:
- Operational excellence
- Security
- Reliability
- Performance efficiency
- Cost optimization
- Sustainability 

Based on this information, I can answer the original question

</thinking>

<answer>

The six pillars of the AWS Well-Architected Framework are:
- Operational excellence
- Security
- Reliability
- Performance efficiency
- Cost optimization
- Sustainability

</answer>