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SquiggleAI

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Frontmatter
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Raw MDX Source
---
title: SquiggleAI
description: An LLM-powered tool for generating probabilistic models in Squiggle from natural language descriptions. Uses Claude Sonnet 4.5 with 20K token prompt caching to produce 100-500 line models within 20 seconds to 3 minutes. Integrated directly into Squiggle Hub, SquiggleAI addresses the challenge that domain experts often struggle with programming requirements for probabilistic modeling.
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llmSummary: SquiggleAI is an LLM tool (primarily Claude Sonnet 4.5) that generates probabilistic Squiggle models from natural language, using ~20K tokens of cached documentation to produce 100-500 line models in 20 seconds to 3 minutes. While it lowers barriers for domain experts to create uncertainty models, it's a workflow improvement tool rather than a core AI safety intervention.
lastEdited: "2026-01-29"
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entityType: approach
---
import {DataInfoBox, Mermaid, EntityLink, DataExternalLinks} from '@components/wiki';

## Quick Assessment

| Dimension | Assessment | Evidence |
|-----------|------------|----------|
| **Innovation** | High | First LLM integration for probabilistic programming language |
| **Practical Impact** | Growing | Lowers barrier to quantitative reasoning for domain experts |
| **Technical Maturity** | Stable | Multiple model options, reliable prompt caching |
| **Integration** | Seamless | Built directly into Squiggle Hub platform |
| **Accessibility** | High | Natural language interface, no programming required |
| **Output Quality** | Good | 100-500 line models with reasonable structure |

## Project Details

| Attribute | Details |
|-----------|---------|
| **Name** | SquiggleAI |
| **Organization** | <EntityLink id="E238">QURI</EntityLink> (Quantified Uncertainty Research Institute) |
| **Lead** | Ozzie Gooen |
| **Launched** | 2024 |
| **Primary Model** | Claude Sonnet 4.5 |
| **Platform** | Integrated into Squiggle Hub |
| **Website** | [squiggle-language.com/docs/Ecosystem/SquiggleAI](https://www.squiggle-language.com/docs/Ecosystem/SquiggleAI) |

## Overview

[SquiggleAI](https://www.squiggle-language.com/docs/Ecosystem/SquiggleAI) integrates <EntityLink id="E186">large language models</EntityLink> to assist with probabilistic model creation, addressing a critical barrier QURI identified through years of experience building forecasting tools: **even highly skilled domain experts frequently struggle with basic programming requirements** for probabilistic modeling.

A subject matter expert might have deep knowledge about AI timelines, pandemic risk, or cost-effectiveness of interventions, but lack the programming fluency to translate their mental model into executable <EntityLink id="E286">Squiggle</EntityLink> code. SquiggleAI bridges this gap by accepting natural language descriptions and generating complete, runnable probabilistic models.

The system uses prompt caching to embed approximately 20,000 tokens of information about the Squiggle language—syntax, distributions, best practices—ensuring the LLM has deep knowledge of the domain-specific language. Most workflows complete within 20 seconds to 3 minutes, producing models typically 100-500 lines long depending on the model used.

### Design Philosophy

SquiggleAI is designed around the principle that **domain expertise should not be bottlenecked by programming ability**. The tool aims to:

1. **Lower the barrier** to quantitative reasoning for non-programmers
2. **Accelerate model creation** even for experienced Squiggle users
3. **Teach by example** through generated code that users can read and modify
4. **Preserve human judgment** by making models transparent and editable

This philosophy aligns with QURI's broader mission of making uncertainty quantification accessible to altruistic decision-makers.

## Model Support

SquiggleAI supports multiple LLM backends:

| Model | Status | Output Size | Speed | Best For | Cost |
|-------|--------|-------------|-------|----------|------|
| **Claude Sonnet 4.5** | Primary | ≈500 lines | Medium | Complex multi-variable models | $$$ |
| **Claude Haiku 4.5** | Available | ≈150 lines | Fast | Quick prototypes | $ |
| **Grok Code Fast 1** | Available | ≈200 lines | Fast | Alternative provider | $$ |
| **Claude Sonnet 3.5** | Legacy | ≈200 lines | Medium | Stable fallback | $$ |

The default is Claude Sonnet 4.5, which produces the most sophisticated and detailed models but at higher API cost. Users can switch models based on their needs.

### Prompt Caching

SquiggleAI uses **prompt caching** to cache approximately 20,000 tokens of Squiggle documentation with each request. This means:

- The LLM has consistent, deep knowledge of Squiggle syntax
- API costs are reduced (cached tokens are cheaper)
- Response quality is more reliable
- Context is preserved across conversation turns

## Capabilities

### Model Generation

Describe a problem in natural language, receive executable Squiggle code:

**Example prompt**:
> "Estimate the cost-effectiveness of hiring an AI safety researcher. Consider their salary, overhead costs, probability they produce useful research, and value of that research if successful."

**Output**: ~300 line Squiggle model with:
- Variable definitions for each uncertain input
- Distribution choices justified by context
- Sensitivity analysis visualization
- Clear comments explaining logic

### Iterative Refinement

Conversation-based model improvement:

**Follow-up prompt**:
> "Add more uncertainty to the timeline assumptions and break down the research impact by type (interpretability vs alignment)."

**Output**: Modified model with requested changes, preserving existing structure.

### Fermi Estimation

Generate complete uncertainty models from vague questions:

| Question Type | Example | Output |
|---------------|---------|--------|
| **Classic Fermi** | "How many piano tuners in Chicago?" | ≈100 line model with breakdown |
| **Technology adoption** | "When will 50% of US households have solar panels?" | ≈200 line S-curve model |
| **Market sizing** | "Total addressable market for AI coding assistants?" | ≈250 line market model |

### Code Debugging

Identify and fix syntax and logic errors:

**User**: "My model gives negative probabilities for the success rate"

**SquiggleAI**: Identifies issue with distribution domain, suggests `truncate(distribution, 0, 1)`

### Model Explanation

Explain what existing Squiggle code does:

**Input**: Inherited 300-line model from another researcher

**Prompt**: "Explain what this model is calculating and what the key assumptions are"

**Output**: Plain-language summary with identified cruxes

## Integration with Squiggle Hub

SquiggleAI is **directly accessible from within Squiggle Hub**, allowing users to:

1. Generate models from the "New Model" interface
2. Refine existing models through chat interface
3. Immediately save generated models to their Hub account
4. Version control AI-assisted iterations
5. Share completed models publicly or keep private

### Privacy Defaults

SquiggleAI outputs on Squiggle Hub are **private by default**. Users who want to share models or make them public can explicitly do so by creating new public models. This privacy-first approach encourages experimentation without concern about incomplete drafts being visible.

## Technical Implementation

### Architecture

```
User Prompt
    ↓
Prompt Engineering Layer
(Adds cached Squiggle docs, examples, constraints)
    ↓
LLM API (Claude Sonnet 4.5)
    ↓
Post-Processing
(Syntax validation, auto-fixes)
    ↓
Executable Squiggle Code
```

### Prompt Engineering

The system prepends each user request with:

| Component | Tokens | Purpose |
|-----------|--------|---------|
| **Squiggle syntax guide** | ≈8,000 | Core language reference |
| **Distribution creation examples** | ≈3,000 | Common patterns |
| **Best practices** | ≈2,000 | Dos and don'ts |
| **Common pitfalls** | ≈1,500 | Error prevention |
| **Example models** | ≈5,500 | Full model templates |
| **Total cached context** | ≈20,000 | Consistent knowledge base |

This large cached context ensures the LLM produces idiomatic Squiggle code rather than generic probabilistic programming.

## Use Cases

### Domain Expert Assistance

| Expert Type | Without SquiggleAI | With SquiggleAI |
|-------------|-------------------|-----------------|
| **Biosecurity researcher** | Cannot quantify risk model | Describes model in English, gets working code |
| **EA charity evaluator** | Builds simple spreadsheet | Generates sophisticated uncertainty model |
| **AI safety researcher** | Rough timeline estimates | Detailed probabilistic timeline model |
| **Policy analyst** | Qualitative cost-benefit | Quantitative cost-benefit with distributions |

### Rapid Prototyping

Experienced Squiggle users leverage SquiggleAI for:

- **First draft generation**: Get 80% of a model in 2 minutes, refine manually
- **Boilerplate reduction**: Generate repetitive parameter definitions
- **Exploration**: Try multiple model structures quickly
- **Documentation**: Generate comments and explanations for existing code

### Teaching and Learning

SquiggleAI serves as a teaching tool:

- New users learn by seeing working examples
- Generated code demonstrates Squiggle idioms
- Explanations help users understand probabilistic reasoning
- Iteration teaches refinement process

## Strengths and Limitations

### Strengths

| Strength | Evidence |
|----------|----------|
| **Lowers barrier** | Non-programmers can create models |
| **Fast iteration** | 20 seconds to 3 minutes per model |
| **Consistent quality** | Prompt caching ensures reliable syntax |
| **Transparent output** | Generated code is readable and editable |
| **Integrated workflow** | Seamless with Squiggle Hub |
| **Multiple model options** | Can choose speed vs sophistication |

### Limitations

| Limitation | Impact | Mitigation |
|------------|--------|------------|
| **Output length bounded** | Claude models limit ≈500 lines | Break complex models into modules |
| **Domain knowledge required** | LLM doesn't know your specific problem | User must provide context and check outputs |
| **Hallucination risk** | May generate plausible but wrong assumptions | Always review and validate generated models |
| **API cost** | Sonnet 4.5 usage adds up | Use Haiku for simpler models |
| **Limited mathematical reasoning** | Struggles with novel math derivations | Use for structure, validate math manually |

## Comparison with Other Approaches

| Approach | Pros | Cons |
|----------|------|------|
| **SquiggleAI** | Fast, accessible, integrated | Requires review, output bounded |
| **Manual Squiggle coding** | Full control, no AI cost | Slow, requires programming skill |
| **ChatGPT + copy/paste** | General purpose | No Squiggle-specific knowledge, awkward workflow |
| **Excel/Guesstimate** | Familiar interface | Limited expressiveness, no distributions |
| **Python + NumPy** | Full power | Much more complex, longer to write |

## Relationship to QURI Ecosystem

SquiggleAI fits into QURI's tool ecosystem:

| Tool | Purpose | SquiggleAI's Role |
|------|---------|-------------------|
| **<EntityLink id="E286">Squiggle</EntityLink>** | Probabilistic language | SquiggleAI generates Squiggle code |
| **Squiggle Hub** | Model hosting platform | SquiggleAI integrated directly |
| **<EntityLink id="E200">Metaforecast</EntityLink>** | Forecast aggregation | SquiggleAI could use Metaforecast data as inputs |
| **<EntityLink id="E385">RoastMyPost</EntityLink>** | LLM blog evaluation | Shared LLM integration experience |

## Funding

As a QURI project, SquiggleAI development is funded through QURI's grants:

| Source | Amount | Period |
|--------|--------|--------|
| **Survival and Flourishing Fund** | \$150K+ to QURI | 2019-2022 |
| **Future Fund** | \$100K to QURI | 2022 |
| **Long-Term Future Fund** | Ongoing to QURI | 2023-present |

SquiggleAI API costs (Claude API usage) are likely covered by QURI's operational budget, though specific allocation is not public.

## Future Directions

Potential enhancements based on the current trajectory:

| Enhancement | Benefit | Challenge |
|-------------|---------|-----------|
| **Multi-model reasoning** | Generate multiple approaches, compare | Higher cost, longer runtime |
| **Automatic sensitivity analysis** | Identify key cruxes programmatically | Requires sophisticated meta-analysis |
| **Citation integration** | Ground generated assumptions in sources | Complex fact-checking, hallucination risk |
| **Collaborative refinement** | Multiple users + AI iterating together | Coordination complexity |
| **Custom expert modes** | Domain-specific prompt engineering | Requires curated expertise per domain |

## External Links

- [SquiggleAI Documentation](https://www.squiggle-language.com/docs/Ecosystem/SquiggleAI)
- [Squiggle Hub](https://squigglehub.org/)
- [EA Forum: Introducing SquiggleAI](https://forum.effectivealtruism.org/posts/jJ4pn3qvBopkEvGXb/introducing-squiggle-ai)
- [QURI Website](https://quantifieduncertainty.org/)
- [Claude API](https://www.anthropic.com/api)