(Cleaned-up, faithful adaptation)
NOTE
Refer to the original Guide for more details:
Prompt Design Fundamentals (Text-only)
Prompt design involves creating natural language requests (prompts) to elicit accurate, high-quality responses from a language model.
Clear and Specific Instructions
Customize model behavior by providing clear, precise instructions. Instructions can be:
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Questions (to get answers)
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Tasks (step-by-step directions)
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Entities (for classification)
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Partial inputs (for completion or continuation)
Example Inputs and Outputs:
| Input Type | Prompt Example | Generated Output Example |
|---|---|---|
| Question | What's a good name for a flower shop specializing in dried bouquets? List 5 names. | 1. Everlasting Blooms 2. Dried & Delightful 3. Petal Preserve 4. Whispers of Wildflowers 5. Forever Blooms |
| Task | List just 5 essential camping items. | 1. Tent 2. Sleeping Bag 3. Sleeping Pad 4. Headlamp 5. First Aid Kit |
| Entity | Classify items as [large, small]: Elephant, Mouse, Snail | Elephant: Large, Mouse: Small, Snail: Small |
| Partial Completion | Provide partial content and examples; the model will complete based on context. |
Constraints
Explicitly state limits or constraints to guide responses clearly:
Example:
Summarize this text in one sentence.
Response Format
Specify exactly how the response should be formatted (JSON, table, bulleted list, paragraph, etc.):
Example:
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Create an outline for an essay about hummingbirds.(model chooses outline format) -
To control outline format precisely, provide partial format as a prompt prefix.
Zero-shot vs Few-shot Prompting
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Zero-shot: No examples given, just instructions.
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Few-shot: Provide 1–3 examples demonstrating the desired format, style, and scope.
Recommendation:
Always include a few-shot example to ensure clearer task understanding.
Optimal Number of Examples
Balance between providing enough examples to clarify the task without causing overfitting.
Patterns vs Anti-patterns
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Positive examples: Show correct behaviors clearly.
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Negative examples (anti-patterns): Avoid explicitly showing incorrect behaviors, as they confuse models.
Positive Example:
Always end haikus with an assertion.
Negative Example:
Don't end haikus with a question. (Avoid this style)
Consistent Formatting
Ensure your few-shot examples consistently use the same formatting and spacing for clarity and predictability.
Adding Context
Include all necessary contextual information in the prompt to avoid generic responses.
Example:
- Provide the troubleshooting guide for a router in the prompt if you want router-specific instructions.
Adding Prefixes
Prefixes clearly label input sections, output formats, or example segments.
Example:
Text: Rhino
Answer: large
Breaking Down Complex Prompts
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Break down instructions into simpler prompts.
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Chain prompts sequentially (output of one becomes input of next).
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Aggregate responses when multiple parallel operations are needed.
Experimenting with Model Parameters
Tune parameters to control model output characteristics:
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max output tokens(response length limit) -
temperature(randomness vs determinism) -
topK(select among K highest probability tokens) -
topP(select tokens until sum probabilities reaches P) -
stop_sequences(stop generation when reached)
Prompt Iteration Strategies
If a prompt isn’t working, consider:
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Rephrasing
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Switching to analogous tasks
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Changing prompt content order
Fallback Responses
If model gives safety fallback (“I can’t help with that…”), try increasing temperature.
Things to Avoid
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Don’t rely on generative models for factual precision.
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Use caution for math and logic-heavy prompts.
Generative Models Under the Hood
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First stage: Deterministic (model predicts probabilities).
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Second stage: Sampling (can be deterministic or stochastic/random based on temperature).
Multimodal Prompting Strategies (Images, Video, etc.)
Multimodal prompts integrate media (images/videos) and text for richer tasks.
Prompt Design Fundamentals (Multimodal-specific)
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Be specific: Clearly instruct the model on what information to extract from media.
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Add examples: Use few-shot examples to clearly show desired outputs.
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Break it down step-by-step: Split complex tasks explicitly or instruct the model to “think step-by-step.”
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Specify output format: JSON, markdown, HTML, etc.
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Put single-image media first: For single-media inputs, place the image/video before text prompt.
Troubleshooting Multimodal Prompts
If output is unclear, generic, or incorrect:
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Directly instruct the model to “describe the image/video first.”
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Explicitly state what information the model should draw from media.
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Ask the model to explain its reasoning or interpretation.
Troubleshooting Examples:
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If model misses diaper quantity from image, explicitly instruct:
Use the diaper count from the box image and divide by daily usage to estimate duration. -
For generic outputs (e.g., “What’s common among images?“):
First, describe each image in detail, then explain what's common. -
To troubleshoot understanding, use prompt:
Describe what's in this image/video.
Controlling Output Format (Multimodal-specific)
Clearly specify how outputs should appear:
Example Prompt:
Parse this image’s table into markdown format.
(Model returns markdown table.)
Sampling Parameters for Multimodal Prompts
- Adjust temperature/topK/topP to refine creativity, specificity, or randomness.
Prompt Iteration and Improvement
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Use varied phrasing: Change wording slightly if stuck.
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Analogous tasks: Reframe tasks as multiple-choice or categorization if direct prompts fail.
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Content order: Experiment with changing the sequence of instructions, examples, and context.
Next Steps (Original guide suggestions)
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Experiment in Google AI Studio.
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Further reading: