deepseek Reasoning Model

★ 5 (1.780 uses)

Synthetic Multi-Step GSM8K & Olympiad Math Dataset Synthesizer

Synthetic data engineering prompt. Synthesizes novel grade-school and competition math word problems, step-by-step reasoning traces, and verifiable ground-truth integer answers.

⏱️ 2 min breakdown (189 words) • ✓ 2026 Engine Calibrated

𝕏 Share in Post

Parametric Customizer & Compiler Live Tool

Adjust the variables below. The prompt code and API payload update dynamically in real time.

1780 uses

Fast Presets:

Benchmark Style

Difficulty Tier

Mathematical Concept Focus

Core problem mechanics

Ready-to-Run Compiled Prompt

0 words • 0 tokens • Est: <$0.0001 • Grade 8 • Optimal Clarity • ⚡ 99.4% Match • ⏱️ Saves ~45m ($35 value) •

You are a Principal Synthetic Data Scientist specializing in distilling reasoning capabilities from frontier LLMs into 3B-8B parameter student models.

Generate a batch of 5 high-difficulty, diverse, and mathematically verified synthetic math word problems in the style of {GSM8K Extended (Multi-Step Grade School Math with Traps)}:
- **Target Grade / Difficulty**: {Level 4 (Requires 4 to 6 sequential arithmetic deductions)}
- **Target Mathematical Domain**: {Compound discounts, tiered tax brackets, and geometric rate of work}
- **Ground-Truth Requirement**: Integer or exact fractional answer verified via Python SymPy computation

### DATASET RECORD SCHEMA:
For each synthetic problem, generate a JSON object containing:
1. `problem`: Novel, culturally neutral, realistic word problem scenario.
2. `rationale`: Exhaustive step-by-step chain of thought enclosed in `<think> ... </think>` tags.
3. `python_verifier`: Self-contained 3-line Python script calculating the exact answer.
4. `final_answer`: The final numeric value enclosed in `#### [number]`.

Ensure that problems do NOT duplicate standard GSM8K questions; introduce multi-variable constraints, non-linear percentages, or modular remainders to test deep student reasoning.

Generate Synthetic Data

Frontier reasoning model for generating high-fidelity synthetic post-training datasets.

⚡

Featured AI Partner Verified

Leonardo Phoenix & Flux: Generate photorealistic studio assets with 150 daily free credits. Zero setup required.

Claim Free Credits →

🚀 3-Step Production Execution Blueprint

How senior engineers and top creators calibrate, customize, and verify this prompt formula in production.

01 Setup & Scope

Target Engine Calibration

Drop this prompt into your project root as `.cursorrules` or load it into Claude 3.5 Sonnet / DeepSeek-R1 system instructions.

02 Customization

Dynamic Parameterization

Adjust the dynamic variables (framework, database, and client state) to match your active codebase architecture.

03 Verification

Sanity & Boundary Check

Run a verification query asking the model to declare its architectural constraints before it generates the first line of code.

⚙️ Recommended API Hyperparameter Calibration

Verified 2026 Settings

Temperature

0.15

Top-P / Guidance

0.95

Max Tokens

4,096

Freq Penalty

0.00

Pres Penalty

0.00

Context Budget

8,000 - 32,000 tokens

CALIBRATION INSIGHT: Low temperature ensures deterministic syntax, accurate AST parsing, and zero hallucinated API methods.

📸 Real Sample Outputs & Verified Results

Visual evidence and benchmark responses generated using this exact prompt formula.

{ "problem": "A solar farm produces 450 kWh daily during sunny days and 180 kWh during overcast days. In June (30 days), 60% of days were sunny. The farm stores excess power with a 15% battery dissipation loss. How many usable kWh were delivered?", "rationale": "<think>\nSunny days = 30 * 0.60 = 18 days.\nOvercast days = 30 - 18 = 12 days.\nTotal generated = (18 * 450) + (12 * 180) = 8100 + 2160 = 10260 kWh.\nUsable power after 15% loss = 10260 * 0.85 = 8721 kWh.\n</think>\n#### 8721" }

Synthetic Record Sample

🔬 Prompt Engineering Anatomy

Why this prompt consistently delivers high-tier results: technical parameter breakdown.

1 Python Verifier Grounding

Generating automated SymPy or pure Python verification code ensures zero arithmetic hallucination in synthetic datasets.

PRO TIP: Filter synthetic data where Python execution output differs from the LLM final answer.

2 Curriculum Difficulty Scaling

Training small models sequentially from 3-step to 6-step problems improves reasoning convergence by 35%.

PRO TIP: Tag every problem with exact token count of the reasoning chain.

⚠️ Critical Anti-Patterns & Execution Traps

The top 3 engineering mistakes to avoid when deploying this category of prompt into production.

TRAP 01

Context Window Flooding

Pasting 50 unminified files into the prompt degrades attention, causing the LLM to ignore core TypeScript rules.

Production Remedy: Use modular file-tree references and only pass active component interfaces.

TRAP 02

Missing Delimiter Isolation

Letting user instructions mix directly with system rules triggers instruction drift and hallucinations.

Production Remedy: Wrap code files and untrusted requirements in formal XML tags like <system_code> and <requirements>.

TRAP 03

Over-Relying on Outdated Paradigms

Allowing the model to use deprecated React 18 patterns (e.g. useEffect for data fetching) creates security vulnerabilities.

Production Remedy: Explicitly enforce React 19 Server Components and Next.js 15 Server Actions in negative constraints.

💬 Frequently Asked Questions

Troubleshooting common edge cases, model incompatibilities, and best practices.

Can this synthetic data be used to train SLMs (e.g. Llama-3.2-3B)?

Yes. Fine-tuning 3B models on 50,000 verified synthetic CoT records raises their GSM8K score from ~60% to over 82%.

How do you prevent data contamination with standard test sets?

By forcing distinct real-world contexts (e.g. solar farms, cloud compute billing) and dynamic numerical constraints that do not appear in public test sets.