Chain-of-thought reasoning

Our annotators structure input prompts to enhance language models' performance on tasks requiring logic, calculation, and decision-making. By incorporating chain-of-thought prompting techniques, they guide models to detail their reasoning step by step, ensuring more accurate and reliable outputs. This approach not only improves task performance but also enhances transparency and interpretability, making it easier to trust and verify the model's conclusions.

Enhance logical and analytical model output

Step-by-step reasoning

Our annotators guide LLMs to provide detailed, step-by-step explanations for complex tasks. This method ensures that the reasoning process is transparent and logical, enhancing the reliability of the model's conclusions.

Improving calculation accuracy

By structuring prompts to include intermediate steps, our annotators help LLMs improve the accuracy of calculations. This detailed approach reduces errors and increases confidence in the results provided by the model.

Logical decision-making

Our annotators craft prompts that encourage LLMs to break down decision-making processes. This helps in generating more thoughtful and well-reasoned outcomes, crucial for applications requiring critical thinking and sound judgment.

Custom prompt engineering

We create tailored prompts that align with specific tasks, ensuring that the model's responses are both relevant and insightful. This customization enhances the model’s ability to handle a wide range of logical and analytical challenges.

Handling complex queries

Our annotators equip LLMs to tackle intricate queries by embedding reasoning instructions within prompts. This allows the model to handle complex questions with greater depth and precision, providing users with comprehensive and understandable answers.

Impressions from our community


"We had a novel task that we needed to complete on a short time scale. The Pareto team worked very closely with us to onboard, disambiguate, and scale up for fast task completion. We're continuing to work with the same pool of high quality raters for our newer tasks."

Prajit Ramachandran, Founding Researcher

Prajit Ramachandran

Founding Researcher @ Character.AI

Join hundreds of fast-growing teams who count on Pareto to ensure factuality and honesty in their language models.

Fine-tune models for logical and analytical reasoning in various contexts

Complex mathematical problem solving


A user asks the model to solve a complex math problem: "What is the derivative of (3x^3 - 5x^2 + 7)?" The model provides the correct derivative but fails to explain the steps involved, making it difficult for the user to understand the solution process.


An expert annotator revises the prompt to: "What is the derivative of (3x^3 - 5x^2 + 7)? Explain your answer step by step." The model then details each step, explaining how the derivative is calculated.


Provides a clear and understandable explanation of the mathematical process.Helps users learn and verify the steps involved in solving the problem, enhancing their comprehension and trust in the model's capabilities.

Logical decision-making in business strategies


A business user seeks advice on choosing between two marketing strategies. The model provides a final recommendation but lacks a detailed explanation of the reasoning behind the choice.


An expert annotator structures the prompt to: "Compare the benefits of Strategy A versus Strategy B for our marketing campaign. Explain your reasoning step by step." The model then outlines the pros and cons of each strategy, followed by a logical reasoning process leading to the final recommendation.


Ensures that business decisions are based on a transparent and well-reasoned analysis.Enhances the user’s confidence in the model’s recommendations by providing a clear rationale.

Scientific research analysis


A researcher asks the model to summarize a complex scientific paper. The model provides a brief summary but omits the logical flow of the research findings.


An expert annotator adjusts the prompt to: "Summarize the findings of this scientific paper and explain the research process step by step." The model then details each stage of the research, from hypothesis to conclusion, highlighting the logical flow.


Offers a comprehensive understanding of scientific research, making it easier for researchers to grasp the study's methodology and results.Promotes transparency and accuracy in scientific communication, fostering trust in the model's outputs.

How it works


Describe your project

We help you develop clear project guidelines, determine the ideal evaluation team, and set a cost-effective hourly rate to fit your timeline


Match with top evaluators

We assemble your team same-day from our vetted network. If you have unique needs, we can find the right experts in just 3–5 days


Project managed & quality assured

We support data evaluators to deliver the highest quality data with paid trials, expert review and feedback, gold standard items, and more QA techniques

Built by and for a new generation of data workers

The infrastructure behind human data collection is antiquated. We’ve joined forces with seasoned data labelers, annotators, prompt engineers, and crowdwork researchers to redefine the relationship between workers and requesters.

Pareto operates on the principles of equitable compensation, collaborative management, and expert evaluation and feedback. Our mission is to empower talented and diverse professionals worldwide to contribute to AI training.

Enterprise-grade scale and quality

Fully managed service

Our project managers are just a Slack message or email away.

24/7 Global support

Our distributed team of experts offer assistance around the clock.


Up-front and transparent pricing tailored to your project requirements.

Common Questions

How long does it take to get set up with Pareto?


Our team can have you up and running with Pareto in as little as 24 hours. Interested in getting started? Speak with our team!

Can I use Pareto for a one-time project, or do I need to commit to a long-term contract?


You do not need to commit to a long-term contract. Pareto offers cost-effective and on-demand pricing. Fair hourly rates are set based on the expertise and skills of the workforce you need.

What measures does Pareto take to ensure work quality?


We create precise guidelines and cost estimates upfront. Your project manager reviews project timelines, costs, and success criteria with you before each batch of tasks to ensure results that meet or surpass your expectations.

Does Pareto offer post-project support?


Absolutely. Your project manager remains accessible to assist with any inquiries or issues that may arise following the project's completion. Should any outcomes fall short of your project's requirements, inform us within a five-day period after submission, and we'll either revise the work or provide a credit refund.

Can Pareto assist with international projects outside the US?


Pareto collaborates with companies worldwide, adapting to different time zones and team requirements. We have experience in handling international projects with ease. Our data experts are distributed across the globe, ensuring uninterrupted and reliable service around the clock.

How experienced is the team at Pareto?


Pareto boasts an elite network of prompt engineers, annotators, and evaluators with expertise in finance, healthcare, engineering, and more. We also recruit, train, and upskill people from all walks of life, striving to create a rewarding career in data work for anyone with the right ambition.

What types of projects can Pareto support?


Pareto is adept at handling a diverse array of manual, data-centric tasks and operations for AI companies. From fine-tuning LLM's with human feedback to data curation and labeling, we do it all. Just share your objectives with us, and we'll customize our AI-driven workflows to suit your specific requirements.

Ensure factual accuracy for your models

Explore other use cases

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Get ready to join forces!

Already set up? Message your project manager.

By continuing, you agree to receive communications from Pareto and authorize us to process your personal information in compliance with our privacy policy.

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See how step-by-step reasoning can boost model output

See how step-by-step reasoning can boost model output