> ## Documentation Index
> Fetch the complete documentation index at: https://docs.perceptron.inc/llms.txt
> Use this file to discover all available pages before exploring further.

# Prompting reference

> Copy-paste prompts and code for each SDK primitive on Isaac 0.2.

<Callout type="info">
  Isaac 0.2 triggers thinking and grounding through `<hint>...</hint>` system messages. See the [API reference](/isaac-0.2/api-reference/endpoint/chat-completions) for details.
</Callout>

## Quick reference

| Task                  | SDK Helper                  | Optimal Prompt                                                                                                |
| --------------------- | --------------------------- | ------------------------------------------------------------------------------------------------------------- |
| **Concise caption**   | `caption(style="concise")`  | `Provide a concise, human-friendly caption for the upcoming image.`                                           |
| **Detailed caption**  | `caption(style="detailed")` | `Provide a detailed caption describing key objects, relationships, and context in the upcoming image.`        |
| **OCR**               | `ocr()`                     | System: `You are an OCR system. Accurately detect, extract, and transcribe all readable text from the image.` |
| **General detection** | `detect()`                  | `Your goal is to segment out the objects in the scene`                                                        |
| **Class detection**   | `detect(classes=[...])`     | `Your goal is to segment out the following categories: {categories}`                                          |
| **Visual Q\&A**       | `question()`                | Pass your question directly as user content                                                                   |
| **Grounded Q\&A**     | `question(expects="box")`   | Same question, model returns boxes with answers                                                               |
| **Counting**          | `question()`                | `How many {objects} are there? Point to each.`                                                                |

***

## Grounding on Isaac 0.2 (`<hint>` syntax)

Place hint values inside a system-role message. Multiple hints can share one `<hint>...</hint>` tag, separated by spaces.

| Hint                   | Output              |
| ---------------------- | ------------------- |
| `<hint>BOX</hint>`     | Bounding boxes      |
| `<hint>POINT</hint>`   | Points              |
| `<hint>POLYGON</hint>` | Polygons            |
| `<hint>THINK</hint>`   | Reasoning trace     |
| `<hint>FOCUS</hint>`   | Internal focus tool |

### Example: boxes

```bash theme={null}
curl -X POST "https://api.perceptron.inc/v1/chat/completions" \
  -H "Content-Type: application/json" \
  -H "Authorization: Bearer $PERCEPTRON_API_KEY" \
  -d '{
  "model": "isaac-0.2-2b-preview",
  "messages": [
    { "role": "system", "content": [{"type": "text", "text": "<hint>BOX</hint>"}] },
    { "role": "user",
      "content": [
        {"type": "image_url", "image_url": {"url": "<image-url>"}},
        {"type": "text", "text": "Find all the safety equipment"}
      ]
    }
  ]
}'
```

### Example: reasoning

```bash theme={null}
curl -X POST "https://api.perceptron.inc/v1/chat/completions" \
  -H "Content-Type: application/json" \
  -H "Authorization: Bearer $PERCEPTRON_API_KEY" \
  -d '{
  "model": "isaac-0.2-2b-preview",
  "messages": [
    { "role": "system", "content": [{"type": "text", "text": "<hint>THINK</hint>"}] },
    { "role": "user",
      "content": [
        {"type": "image_url", "image_url": {"url": "<image-url>"}},
        {"type": "text", "text": "Count the number of cars, excluding buses. Explain your reasoning."}
      ]
    }
  ]
}'
```

### Example: counting (boxes + thinking)

```bash theme={null}
curl -X POST "https://api.perceptron.inc/v1/chat/completions" \
  -H "Content-Type: application/json" \
  -H "Authorization: Bearer $PERCEPTRON_API_KEY" \
  -d '{
  "model": "isaac-0.2-2b-preview",
  "messages": [
    { "role": "system", "content": [{"type": "text", "text": "<hint>BOX THINK</hint>"}] },
    { "role": "user",
      "content": [
        {"type": "image_url", "image_url": {"url": "<image-url>"}},
        {"type": "text", "text": "Count the helmets on visible workers and box each one."}
      ]
    }
  ]
}'
```

***

## Advanced: `@perceive` decorator

The Python SDK wraps the chat-completions endpoint with a typed decorator that handles hint setup and result parsing for you.

### With reasoning

```python theme={null}
from perceptron import configure, perceive, image, text

configure(provider="perceptron", api_key="YOUR_API_KEY")

@perceive(model="isaac-0.2-2b-preview", reasoning=True)
def analyze(photo):
    return image(photo) + text("Identify all the colors in this scene")

result = analyze("scene.jpg")
print(result.reasoning)  # chain-of-thought trace
print(result.text)
```

### With structured output (Pydantic)

```python theme={null}
from typing import Literal
from pydantic import BaseModel, Field
from perceptron import configure, perceive, pydantic_format, image, text

configure(provider="perceptron", api_key="YOUR_API_KEY")

class SceneAnalysis(BaseModel):
    scene_type: Literal["urban", "nature"]
    main_subjects: list[str] = Field(description="Primary objects in the scene")
    mood: Literal["energetic", "peaceful", "tense"]
    time_of_day: Literal["day", "night", "unknown"]

@perceive(model="isaac-0.2-2b-preview", response_format=pydantic_format(SceneAnalysis))
def analyze_scene(photo):
    return image(photo) + text("Analyze this scene. Output in JSON with scene type, subjects, mood and time of day.")

scene = analyze_scene("photo.jpg")
print(f"Scene type: {scene.scene_type}")
print(f"Subjects: {scene.main_subjects}")
print(f"Mood: {scene.mood}")
print(f"Time: {scene.time_of_day}")
```