Introduction: Tailoring Your Approach
Different AI models have different capabilities. A prompt that works perfectly for one model might fail with another. Understanding these differences is key to effective prompt engineering.
Consider a basic request to create an image of a sunset over mountains. With a text-only model, this prompt produces a description. With a text-to-image model, it generates a visual representation. The same words yield entirely different results.
This chapter explores how to adapt your prompt engineering techniques for specialized AI models. We’ll examine text-to-image systems, code generation models, multimodal AI, and retrieval-augmented models. Each requires unique approaches to achieve optimal results.
By learning these specialized techniques, you’ll expand your prompt engineering toolkit. You’ll be able to work effectively across the growing ecosystem of AI capabilities. This flexibility becomes increasingly valuable as AI systems continue to diversify and evolve.
Text-to-Image Models
Text-to-image models convert written descriptions into visual content. They require prompts that effectively communicate visual elements.
Core Principles for Visual Prompts
When working with text-to-image models, focus on these key aspects:
Visual Details and Attributes
Include specific visual elements:
Instead of: "A mountain landscape"
Use: "A mountain landscape with snow-capped peaks, pine forests, a clear blue lake in the foreground, and wispy clouds in a sunset sky with orange and purple hues"
More visual details generally produce more specific results.
Style and Medium Specifications
Define the artistic approach:
"A portrait of an elderly fisherman in the style of oil painting by Rembrandt, with dramatic lighting, textured brushstrokes, and a dark background"
Common style specifications include:
- Artistic movements (Impressionism, Cubism, Art Deco)
- Technical approaches (watercolor, digital art, pencil sketch)
- Specific artists’ styles (in the style of Monet, Picasso, Warhol)
- Photography styles (macro photography, aerial view, long exposure)
Composition Guidance
Direct the visual arrangement:
"A serene garden with a central stone pathway leading to a small wooden bench, framed by blooming cherry trees, with soft morning light coming from the right side"
Composition elements to specify:
- Perspective (bird’s eye view, low angle, straight-on)
- Framing (close-up, wide shot, rule of thirds)
- Focal point (centered on, focusing on, highlighting)
- Lighting (harsh sunlight, soft diffused light, backlit)
Advanced Text-to-Image Techniques
These approaches help refine and control image generation.
Weighted Prompting
Use emphasis indicators to prioritize elements:
"A forest scene with (tall redwood trees:1.3) and (morning mist:1.2) with (small woodland creatures:0.8) near a (flowing stream:1.1)"
The numbers indicate relative importance. Higher values (above 1.0) emphasize elements. Lower values (below 1.0) de-emphasize them.
Negative Prompting
Specify what to exclude:
"A modern kitchen interior with marble countertops and wooden cabinets.
Negative prompt: blurry, distorted proportions, unrealistic shadows, text, watermarks, signatures"
Negative prompts help avoid common issues in generated images.
Quality and Rendering Boosters
Include technical parameters:
"A futuristic cityscape with flying vehicles and tall glass buildings.
High resolution, 8K, photorealistic, detailed, sharp focus, professional photography"
These terms signal the model to prioritize technical quality in the output.
Common Challenges and Solutions
Problem: Unrealistic Anatomy
Human figures often have distorted features.
Solution:
"Portrait of a female firefighter in uniform, accurate human anatomy, proper proportions, photorealistic, detailed facial features"
Problem: Inconsistent Style
Mixed visual styles create jarring results.
Solution:
"A children's book illustration of a friendly dragon, consistent watercolor style throughout, cohesive color palette of blues and greens, uniform line work"
Problem: Text Rendering
Text in images often appears distorted.
Solution: Either avoid requesting text in images or use post-processing tools to add text after generation.
Practical Example: Evolution of an Image Prompt
Let’s see how a prompt evolves from basic to advanced:
Basic:
"A cat in a garden"
Improved:
"A ginger tabby cat sitting in an English cottage garden"
Detailed:
"A fluffy ginger tabby cat sitting on a stone path in an English cottage garden with blooming roses, lavender, and daisies under dappled sunlight"
Advanced:
"A fluffy ginger tabby cat sitting on a moss-covered stone path in an overgrown English cottage garden. Blooming roses, lavender, and daisies surround the path. Dappled sunlight filtering through old oak trees.
Style: Photorealistic, high detail, soft natural lighting, shallow depth of field.
Negative prompt: distorted features, blurry, oversaturated colors, unnatural poses"
Each version adds more control and specificity.
Code Generation Models
Code generation models create programming solutions from descriptions. They require prompts that effectively communicate technical requirements.
Core Principles for Code Prompts
Problem Specification Clarity
Clearly define the coding task:
"Write a Python function that takes a list of integers as input and returns the sum of all even numbers in the list."
Key elements to include:
- Programming language
- Input parameters with types
- Expected output with type
- Core functionality
Context and Constraints
Provide relevant technical context:
"Write a JavaScript function to fetch data from an API and display results in a table. The code will run in a browser environment with ES6 support. The function should handle errors and show a loading state."
Useful contexts to specify:
- Environment (browser, server, mobile)
- Version constraints (Python 3.8+, ES6)
- Performance requirements
- Error handling expectations
- Compatibility needs
Code Style and Standards
Specify coding conventions:
"Create a React component for a user registration form following these guidelines:
- Use functional components with hooks
- Follow camelCase naming convention
- Include prop validation
- Add meaningful comments
- Follow accessibility (a11y) best practices"
Advanced Code Generation Techniques
Sample Input/Output Pairs
Provide examples of expected behavior:
"Write a Python function to convert temperature between Celsius and Fahrenheit. The function should take two parameters: a float value and a string indicating the unit ('C' or 'F').
Examples:
convert_temperature(32.0, 'F') should return 0.0 (Celsius)
convert_temperature(0.0, 'C') should return 32.0 (Fahrenheit)
convert_temperature(100.0, 'C') should return 212.0 (Fahrenheit)"
Skeleton Code Completion
Provide a partial implementation:
"Complete the following Python function that implements binary search:
```python
def binary_search(arr, target):
left = 0
right = len(arr) - 1
while left <= right:
# TODO: Calculate mid point
# TODO: Check if target is found
# TODO: Update left or right pointers
# TODO: Return appropriate value if target is not found
#### Iterative Refinement
Break complex problems into stages:
“Let’s build a simple task management application in JavaScript step by step:
- First, create a Task class with properties for id, title, description, and status
- Next, implement a TaskManager class that can add, remove, and update tasks
- Then, add a method to filter tasks by status
- Finally, implement local storage functionality to persist tasks”
### Code Documentation Prompts
Generate documentation for existing code:
“Write comprehensive JSDoc comments for the following JavaScript function:
function processData(rawData, options = {}) {
const { normalize = true, filter = null } = options;
let result = [...rawData];
if (normalize) {
result = result.map(item => ({
...item,
name: item.name.toLowerCase(),
value: Number(item.value)
}));
}
if (filter) {
result = result.filter(item => filter(item));
}
return result;
}
### Testing Prompts
Generate test cases for code:
“Write unit tests using Jest for the following React hook:
function useLocalStorage(key, initialValue) {
const [storedValue, setStoredValue] = useState(() => {
try {
const item = window.localStorage.getItem(key);
return item ? JSON.parse(item) : initialValue;
} catch (error) {
console.error(error);
return initialValue;
}
});
const setValue = value => {
try {
const valueToStore = value instanceof Function ? value(storedValue) : value;
setStoredValue(valueToStore);
window.localStorage.setItem(key, JSON.stringify(valueToStore));
} catch (error) {
console.error(error);
}
};
return [storedValue, setValue];
}
Include tests for initialization, updating values, and error handling.”
### Debugging Prompts
Help find and fix bugs:
“The following Python code is supposed to create a simple calculator but has bugs. Identify and fix all issues:
def add(x, y):
return x + y
def subtract(x, y):
return x - y
def multiply(x, y)
return x * y
def divide(x, y):
return x / y
print("Select operation:")
print("1. Add")
print("2. Subtract")
print("3. Multiply")
print("4. Divide")
choice = input("Enter choice (1/2/3/4): ")
num1 = input("Enter first number: ")
num2 = input("Enter second number: ")
if choice == '1':
print(num1,"+",num2,"=", add(num1,num2))
elif choice == '2':
print(num1,"-",num2,"=", subtract(num1,num2))
elif choice == '3':
print(num1,"*",num2,"=", multiply(num1,num2))
elif choice == '4':
print(num1,"/",num2,"=", divide(num1,num2))
else:
print("Invalid input")
### Common Challenges and Solutions
#### Problem: Hallucinated Functions
Models sometimes use functions that don't exist in the specified environment.
Solution:
“Write Python code to analyze text sentiment using only the standard library (no external packages). If more advanced functionality is needed, include comments explaining what external libraries would be used.”
#### Problem: Outdated Syntax
Models might generate deprecated patterns.
Solution:
“Write JavaScript code to manipulate the DOM using modern (2024) best practices. Avoid deprecated methods like document.write() and older paradigms like var declarations.”
#### Problem: Security Vulnerabilities
Generated code may contain security issues.
Solution:
“Create a Node.js API endpoint for user authentication. Follow OWASP security best practices including proper password hashing, input validation, and protection against common attacks like SQL injection and CSRF.”
---
## Multimodal AI Models
Multimodal models work with multiple types of input and output. They can process combinations of text, images, audio, and sometimes video.
### Text-and-Image Input Prompts
These prompts combine textual and visual information.
#### Visual Question Answering
Ask questions about specific images:
[Upload image of a complex mechanical device]
“What is this machine? Explain its primary components and purpose.”
#### Image-Based Content Creation
Use images as references for text generation:
[Upload image of a landscape painting]
“Write a descriptive poem inspired by this painting. Capture its mood, colors, and setting in your verses.”
#### Visual Analysis Prompts
Request detailed examination of images:
[Upload image of a architectural blueprint]
“Analyze this blueprint. Identify key features, potential design concerns, and approximate square footage.”
### Multi-Step Multimodal Prompts
These prompts chain together different modes of interaction.
#### Sequential Processing
Guide the model through a series of multimodal steps:
Step 1: “Describe the key elements you see in this image.” [Upload image of a city skyline]
Step 2: “Based on these elements, identify which city this is likely to be.”
Step 3: “Now, create a 3-day tourism itinerary for this city.”
#### Comparative Analysis
Request comparison between different media:
“Compare and contrast these two images of coral reefs. Identify visible differences and potential environmental factors that might explain the changes.” [Upload two images of the same coral reef taken years apart]
### Audio Processing Prompts
For models that handle audio input.
#### Transcription Enhancement
Improve automatic transcriptions:
[Upload audio file]
“Transcribe this interview, identify both speakers, and format it as a properly punctuated conversation. Flag any portions where you have low confidence in the transcription.”
#### Audio Analysis
Extract insights from audio:
[Upload music recording]
“Analyze this musical piece. Identify the likely genre, key signature, tempo, prominent instruments, and overall structure.”
### Best Practices for Multimodal Prompting
#### Provide Clear Task Priority
Specify which mode takes precedence:
[Upload image of a handwritten mathematical equation]
“First, transcribe the handwritten equation accurately. Then explain how to solve it step by step, prioritizing mathematical accuracy over general explanations.”
#### Define Output Format for Mixed Inputs
Clarify how different inputs should be processed:
[Upload image of a product]
“Create a marketing description for this product. Format your response as follows:
- Product category and name (based on the image)
- 3-5 key features visible in the image
- 3 potential customer benefits
- A compelling 50-word product description”
#### Acknowledge Limitations
Recognize when to separate tasks:
“I’ll show you an image of a patient’s rash and provide text describing their symptoms. Analyze the visual characteristics from the image, but do NOT attempt to diagnose based on the combined information. Instead, suggest what additional information a doctor would need.” [Upload image]
---
## Retrieval-Augmented Generation (RAG) Models
RAG models combine large language models with external knowledge sources. They access specific information before generating responses.
### Document-Grounded Prompts
These prompts work with specific documents or knowledge bases.
#### Document Question-Answering
Ask questions about specific documents:
“Based solely on the attached quarterly report, what were the company’s three largest expenses? Cite specific page numbers and sections in your answer.” [Upload financial report]
#### Comparative Document Analysis
Ask the model to analyze multiple documents:
“Compare these three research papers on quantum computing. Identify the key methodological differences, major findings, and potential contradictions between them.” [Upload three research papers]
#### Document Summarization with Focus
Direct summarization to specific aspects:
“Summarize the attached legal contract, focusing specifically on payment terms, termination clauses, and intellectual property rights. Format your response as bulleted lists under each category.” [Upload contract]
### Knowledge Base Integration
These prompts leverage external databases or structures.
#### Factual Grounding
Anchor responses in verified information:
“Using only the information in the attached product database, answer the following customer question: ‘Which of your smartphones has the best camera for low-light photography?'” [Upload product database]
#### Domain-Specific Queries
Focus responses on particular knowledge domains:
“Using your medical knowledge base, explain treatment options for Type 2 Diabetes. Include only treatment approaches listed in current clinical guidelines, with particular attention to first-line therapies.”
#### Contextual Relevance Control
Direct the model to focus on particular contexts:
“Using the attached company policies, explain our remote work guidelines to a new employee. Prioritize information relevant to software engineers who will be working across multiple time zones.” [Upload policy documents]
### Temporal Awareness Prompts
These prompts handle time-sensitive information.
#### Recency Requirements
Specify time relevance:
“Using your knowledge base, provide information about COVID-19 vaccination guidelines that is current as of April 2024. Specify the source date for any guidelines you mention.”
#### Historical Context
Request information from specific time periods:
“Based on economic data from 2008-2010, explain the major policy responses to the global financial crisis. Compare these with responses to economic downturns from 1990-2000.”
#### Update-Aware Responses
Acknowledge information currency:
“Explain the current features of the latest iPhone model. Clearly indicate which information comes from your training data and which comes from more recent sources.”
### Common RAG Challenges and Solutions
#### Problem: Citation Overload
Too many citations make responses hard to read.
Solution:
“Provide a summary of recent advances in battery technology for electric vehicles. Cite only the most significant or controversial claims, not commonly accepted facts.”
#### Problem: Source Contradictions
Different sources may present conflicting information.
Solution:
“Analyze the different perspectives on climate change impact on agricultural yields. When sources contradict each other, clearly identify the disagreement and the credentials of the contradicting sources.”
#### Problem: Knowledge Gaps
External sources may have incomplete information.
Solution:
“Answer the following questions about quantum computing based on the attached textbook. If any question cannot be fully answered using only this source, explicitly identify what additional information would be needed.” [Upload textbook]
---
## Model-Specific Optimization Techniques
Different AI models have their own quirks and capabilities. Understanding these can help you craft more effective prompts.
### Temperature and Sampling Parameters
Control response variability:
“Generate five different marketing slogans for an eco-friendly water bottle. Set temperature=0.9 for creative variety.”
“Provide a precise definition of photosynthesis from biology textbooks. Set temperature=0.1 for factual consistency.”
Common parameters:
- Temperature: Higher values (0.7-1.0) increase randomness; lower values (0.1-0.3) increase determinism
- Top-p (nucleus sampling): Controls diversity by considering only the most likely tokens
- Frequency penalty: Reduces repetition of the same words and phrases
- Presence penalty: Encourages the model to include new concepts
### Token Optimization
Manage token usage efficiently:
“Summarize this research paper in exactly 250 words, focusing only on methodology and results.” [Upload paper]
Token-saving techniques:
- Use concise instructions
- Remove unnecessary examples
- Specify maximum output length
- Request bullet points instead of paragraphs when appropriate
- Split very large documents into key sections
### System Messages and Meta-Instructions
Set overall behavior and context:
“You are an expert physics tutor specializing in explaining complex concepts to high school students. Use analogies and visualizations suitable for visual learners. Avoid equations when possible, and when necessary, explain each variable and operation.”
Effective meta-instructions:
- Define expertise level and domain
- Specify communication style
- Set formatting preferences
- Establish response constraints
- Define audience assumptions
### Batch Processing
Process multiple items efficiently:
“Analyze the sentiment (positive, negative, or neutral) of each of the following customer reviews. Present results in a table with the review number, sentiment classification, and confidence level.
- “The service was extremely slow and the staff was rude.”
- “Average experience, nothing special but no major issues.”
- “Absolutely loved the product! Best purchase this year!”
- “Delivery was on time but the product quality was disappointing.”
- “Cool features but overpriced for what you get.”
---
## Domain-Specific Specialized Models
Certain domains have models specifically tuned for their needs. These require tailored prompt approaches.
### Scientific and Research Models
Models optimized for scientific tasks:
“Analyze the following chemical compound structure and predict its solubility in water based on polarity, molecular weight, and functional groups.” [Upload molecular structure]
Effective scientific prompts:
- Use precise technical terminology
- Specify measurement units
- Reference relevant scientific principles
- Request confidence levels with predictions
- Ask for alternative hypotheses
### Legal AI Models
Models trained on legal documents and reasoning:
“Review this commercial lease agreement for potential issues related to:
- Termination conditions
- Maintenance responsibilities
- Renewal terms
- Dispute resolution mechanisms
Format findings by section number, quote the relevant language, explain the potential issue, and suggest alternative language.” [Upload lease document]
Legal prompting best practices:
- Reference specific legal frameworks
- Specify jurisdiction
- Use precise legal terminology
- Request multiple interpretations when appropriate
- Ask for reasoning behind conclusions
### Financial Analysis Models
Models specialized for financial data:
“Analyze these quarterly financial statements for Company X. Identify trends in profitability ratios, liquidity measures, and operational efficiency. Flag any concerning patterns or potential red flags for investors.” [Upload financial statements]
Financial prompt techniques:
- Specify time periods for comparison
- Define specific financial metrics
- Request industry benchmark comparisons
- Ask for forward-looking implications
- Specify risk assessment frameworks
---
## Conclusion: Matching the Model to the Task
The key to effective specialized prompt engineering is understanding each model's strengths and limitations. These determine which tasks it can handle well and which prompting techniques will be most effective.
Text-to-image models need visual details, style guidance, and composition instructions. Code generation models require precise technical specifications and examples. Multimodal models benefit from clear task priorities across different input types. RAG models need guidance on knowledge source integration and citation approaches.
As AI capabilities continue to diversify, prompt engineering becomes increasingly specialized. The techniques that work for general language models may not transfer directly to code generation or image creation systems. Each domain requires its own adaptation of core prompt engineering principles.
The most successful prompt engineers develop a feel for which models best suit particular tasks. They maintain a toolbox of specialized techniques for each model type. This versatility allows them to select the right approach for each situation rather than forcing every task into the same prompting framework.
By mastering these specialized techniques, you'll be better equipped to work across the expanding AI ecosystem. You'll be able to leverage each model's unique capabilities to their fullest potential.
In the next chapter, we'll explore methods for measuring and improving prompt performance. We'll develop frameworks for evaluating prompt effectiveness and systematic approaches to optimizing results across different models and task types.
---
## Key Takeaways from Chapter 6
- Different AI models require tailored prompt engineering approaches based on their specific capabilities
- Text-to-image prompts need visual details, style specifications, and composition guidance
- Code generation prompts benefit from clear problem specifications, constraints, and examples
- Multimodal AI requires clear task priorities and output format definitions across different input types
- RAG models need guidance on knowledge integration, citation practices, and handling of temporal information
- Model-specific parameters like temperature can be used to fine-tune outputs for particular needs
- Domain-specialized models in fields like science, law, and finance have their own optimal prompting patterns
- The most effective prompt engineers adapt their techniques to match each model's unique characteristics
---
## Practical Exercises
### Exercise 1: Text-to-Image Prompt Refinement
**Purpose:** Practice improving image generation prompts
**Instructions:**
1. Start with a basic image concept (e.g., "a futuristic city")
2. Create three increasingly detailed versions of the prompt:
- Basic version (1-5 words)
- Standard version (20-30 words with style and composition)
- Advanced version (50+ words with detailed visual elements, style, negative prompts)
3. If you have access to an image generation system, test each prompt
4. Compare differences in specificity, detail, and control
**Reflection Questions:**
- Which visual elements had the biggest impact on results?
- How did style specifications affect the output?
- What aspects remained difficult to control even with detailed prompts?
### Exercise 2: Code Generation Task Decomposition
**Purpose:** Improve code generation through task breakdown
**Instructions:**
1. Choose a moderately complex programming task
2. Break it down into 4-5 sequential sub-tasks
3. Write prompts for each sub-task that:
- Clearly state what the code should do
- Specify input/output requirements
- Include any necessary context from previous steps
- Request appropriate documentation
4. Test each prompt sequentially, using the output from each step
**Reflection Questions:**
- How did breaking down the task improve the quality of the generated code?
- Which aspects needed the most specific instructions?
- What could you improve in your task decomposition approach?
### Exercise 3: Model Selection Challenge
**Purpose:** Practice matching models to appropriate tasks
**Instructions:**
1. List 10 different real-world tasks requiring AI assistance
2. For each task, identify:
- The most appropriate model type (text-only, text-to-image, code, multimodal, RAG)
- Key prompt components needed for that model type
- Potential challenges with your selected approach
- Alternative models that might also work
3. Create a detailed prompt for your three most interesting tasks
**Reflection Questions:**
- Which factors most influenced your model selection decisions?
- How did prompt requirements differ across model types?
- In what cases might multiple model types be equally effective?