PromptLab: prompt generator for few shot learning

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Mukhtar A.Algezoli
Project Owner

PromptLab: prompt generator for few shot learning

Funding Requested

$35,000 USD

Expert Review
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Overview

PromptLab is a tool designed to generate effective prompts for zero/few-shot learning scenarios, tailored specifically for individuals without coding knowledge. By using natural language techniques, PromptLab aids in crafting precise and contextual prompts that enable models to perform tasks without any training. PromptLab will allow non-programmers to take the full advantage of of the current state-of-the art (SOTA) large language models like GPT, Gemini, Claude, and Llama.

Proposal Description

How Our Project Will Contribute To The Growth Of The Decentralized AI Platform

This project will enable non-programmers to utilize few-shot learning on large language models (LLMs), enhancing accessibility and allowing the AI platform to flourish.

Our Team

Every member of our team is an AI expert, currently working at prestigious companies. Each holds a graduate degree in Artificial Intelligence from a top UK or German university.

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AI services (New or Existing)

PromptLab

Type

New AI service

Purpose

PromptLab, a user-friendly platform designed to streamline the process of interacting with LLMs using zero/few shot learning. PromptLab empowers users to easily specify their desired task, define the number of examples per prompt, and provide the examples. The platform then rigorously evaluates multiple prompts to identify the most effective one for the given task. Once the optimal prompt is determined, users can leverage PromptLab to effortlessly generate outputs using this fine-tuned prompt.

AI inputs

Task configuration (task type, number of examples, the examples, model type (or API))

AI outputs

best prompt, output for each prompting operation

The core problem we are aiming to solve

Currently, few-shot learning, is inaccessible to non-programmers due to the specific formatting required for choosing how to input the examples. Moreover, Large Language Models (LLMs) exhibit significant performance variations depending on the prompts used, and these optimal prompts can differ across models.

Our specific solution to this problem

To address the challenges above, we introduce PromptLab, a user-friendly platform designed to streamline the process of interacting with LLMs using zero/few shot learning. PromptLab empowers users to easily specify their desired task, define the number of examples (shots) per prompt, and provide the examples. The platform then rigorously evaluates multiple prompts to identify the most effective one for the given task. Once the optimal prompt is determined, users can leverage PromptLab to effortlessly generate outputs using this fine-tuned prompt.

Project details

Large language models (LLMs) are revolutionizing the way we interact with and utilize information. Their ability to understand and generate human-like text has far-reaching implications across various fields. However, the true potential of LLMs is unlocked through the innovative approaches of zero-shot and few-shot learning.

Zero-shot and few-shot learning are techniques that enable LLMs to perform tasks with minimal or no direct training examples.

Zero-shot learning: In this approach, an LLM can perform a task it has never seen before by relying on its general knowledge and understanding of language. For example, a model might be able to translate a sentence from English to French even if it has never been explicitly trained on that specific language pair.

Few-shot learning: This approach involves providing the LLM with a small number of examples to demonstrate the task. The model then uses its ability to generalize from these examples to perform the task on new, unseen data. This is particularly useful for tasks where labeled data is scarce or expensive to obtain.

These learning techniques are essential for making LLMs more versatile and adaptable. They allow models to quickly learn new tasks without requiring extensive retraining, which can save time and resources. Moreover, they enable LLMs to perform tasks in low-resource settings where labeled data is limited.

 

Currently, few-shot learning, is inaccessible to non-programmers due to the specific formatting required for choosing how to input the examples. Moreover, Large Language Models (LLMs) exhibit significant performance variations depending on the prompts used, and these optimal prompts can differ across models.

To address these challenges, we introduce PromptLab, a user-friendly platform designed to streamline the process of interacting with LLMs using zero/few shot learning. PromptLab empowers users to easily specify their desired task, define the number of examples (shots) per prompt, and provide the examples. The platform then rigorously evaluates multiple prompts to identify the most effective one for the given task. Once the optimal prompt is determined, users can leverage PromptLab to effortlessly generate outputs using this fine-tuned prompt.

 

Competition and USPs

currently there is no tool (that we know of) capable of evaluating different prompts, specify the best prompt, and allow prompting of the model using few shot learning.

Revenue Sharing Model

API Calls

API Description:

Yes, as the last milestone entails.

API Revenue Service

100

API Revenue Percentage

20

API Revenue Year

2025

Proposal Video

Placeholder for Spotlight Day Pitch-presentations. Video's will be added by the DF team when available.

  • Total Milestones

    5

  • Total Budget

    $35,000 USD

  • Last Updated

    18 May 2024

Milestone 1 - API Calls & Hostings

Description

This milestone represents the required reservation of 25% of your total requested budget for API calls or hosting costs. Because it is required we have prefilled it for you and it cannot be removed or adapted.

Deliverables

You can use this amount for payment of API calls on our platform. Use it to call other services or use it as a marketing instrument to have other parties try out your service. Alternatively you can use it to pay for hosting and computing costs.

Budget

$8,750 USD

Milestone 2 - Research best prompts and benchmarks

Description

Large Language Models (LLMs) exhibit significant performance variations depending on the prompts used and these optimal prompts can differ across models. To identify the most effective prompts we need to conduct research and benchmark various models and compile of the best available prompts. Additionally research into the most effective benchmarking methodologies for each task is necessary followed by the acquisition and testing of relevant data for these benchmarks.

Deliverables

A comprehensive report detailing the findings and outcomes of our research.

Budget

$10,000 USD

Milestone 3 - Dashboard Interface Development

Description

Develop an intuitive dashboard interface for users to specify: 1. the task that they want to apply few-shot prompting to (classification summarization question answering etc.). 2. the model that they want to prompt preferably as an API. 3. Specify the number of shots (examples) per prompt and supply those examples.

Deliverables

User-friendly dashboard to config the few shot learning setup

Budget

$8,000 USD

Milestone 4 - Adding user supplied models to the platform

Description

During this phase users can seamlessly integrate their own model (via API) into PromptLab. Once they've set up their task and provided relevant few-shot examples we'll benchmark various prompts to pinpoint the optimal one for their specific use case. Users can then leverage this optimized prompt directly through our platform to efficiently utilize few-shot learning with their model.

Deliverables

adding user specified models to our platform.

Budget

$4,250 USD

Milestone 5 - Integration With SNET

Description

Integrate the developed dashboard and with the SNET marketplace for advanced AI services.

Deliverables

Integration with SNET APIs testing and validation.

Budget

$4,000 USD

Join the Discussion (2)

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2 Comments
  • 1
    commentator-avatar
    Gombilla
    Jun 3, 2024 | 7:05 PM

    Great job with this. However, it is imporatant to note that while PromptLab aims to streamline the process of interacting with large language models (LLMs) for zero/few-shot learning, the effectiveness of the generated prompts may depend on the underlying model's performance and generalization capabilities. There could be challenges in ensuring that the prompts generated by PromptLab consistently produce accurate and meaningful outputs across various tasks and domains. I recoomend that the team conducts rigorous testing and validation where necessary to assess the reliability and performance of the generated prompts. Thanks !

    Reply
    Upvoted by Project Owner
    • 0

      Hello Gomilla, Thank you for your comment. Yes we plan to do a lot of testing for each new model/task, hoping that eventually this process will be automated through the use of a big set of benchmarks.

Reviews & Rating

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8 ratings
  • 1
    user-icon
    Joseph Gastoni
    May 19, 2024 | 8:54 AM

    Overall

    4

    • Feasibility 4
    • Viability 4
    • Desirabilty 4
    • Usefulness 5
    It has potential but requires careful design

    This project proposes PromptLab, a user-friendly platform to generate effective prompts for large language models (LLMs) in zero/few-shot learning scenarios (without coding knowledge). Here's a breakdown of its strengths and weaknesses:

    Feasibility:

    • Moderate-High: The project leverages existing NLP techniques and LLM capabilities. Development effort depends on the chosen evaluation methods and integration with various LLMs.
    • Strengths: The concept builds on established techniques, and development can be efficient with clear technical choices.
    • Weaknesses: Evaluating and selecting optimal prompts requires robust algorithms and handling potential biases in the data.

    Viability:

    • Moderate: Success depends on attracting non-programmer users, effectively integrating with various LLMs, and establishing a strong value proposition.
    • Strengths: The focus on user-friendliness and empowering non-programmers can be attractive.
    • Weaknesses: Competition from existing LLM interfaces, educating users on zero/few-shot learning concepts, and ensuring compatibility with evolving LLMs need to be addressed.

    Desirability:

    • Moderate-High: For non-programmers interested in using LLMs for specific tasks, this can be valuable.
    • Strengths: The project lowers the barrier to entry for non-programmers to leverage advanced LLMs.
    • Weaknesses: Demonstrating the clear benefits and use cases of PromptLab to the target audience is crucial.

    Usefulness:

    • Moderate-High: The project can be very useful if it delivers an intuitive interface, effectively evaluates prompts, and integrates well with various LLMs.
    • Strengths: PromptLab can democratize LLM usage and enable creative applications by non-programmers.
    • Weaknesses: The long-term impact on user adoption, the effectiveness of prompt evaluation methods, and the range of compatible LLMs require evaluation.

    Additional Points:

    • A clear and straightforward user interface is essential for non-programmers.
    • Transparency in how prompts are evaluated and potential biases mitigated is important.
    • Compatibility with a wide range of popular LLMs can increase user base and platform adoption.

    PromptLab has potential but requires careful design and execution. Focusing on a user-friendly interface, robust prompt evaluation methods, clear value proposition for non-programmers, and establishing partnerships with LLM providers can increase the project's value.

    Here are some strengths of this project:

    • Lowers the barrier to entry for non-programmers to leverage the power of large language models.
    • Offers a user-friendly interface for specifying tasks, providing examples, and generating outputs using fine-tuned prompts.
    • Fills a potential gap by automatically evaluating different prompts and selecting the most effective one for a given task.

    Here are some challenges to address:

    • Educating non-programmers about zero/few-shot learning concepts and the value proposition of PromptLab.
    • Developing robust and unbiased algorithms for evaluating the effectiveness of different prompts for various tasks.
    • Ensuring compatibility and integration with a wide range of existing and evolving large language models.

     

  • 0
    user-icon
    HenriqC
    Jun 9, 2024 | 1:26 PM

    Overall

    4

    • Feasibility 4
    • Viability 4
    • Desirabilty 4
    • Usefulness 4
    A simple way to empower a lot of users quickly

    Feasibility

    There is nothing magical in the proposed service. It is very understandable and doable at least in principle. To me the level of the solution’s ultimate effectiveness becomes apparent only after the implementation so it is still a bit of a mystery box for someone with my level of understanding. However, there are no fundamental roadblocks and I expect iterative improvements going forward.

     

    Viability

    The proposers are known figures in the field as well as in DeepFunding. The technical complexities are not overly high and with the provided explanation it is pretty clear what the proposed plan is. Some kind of estimated timelines on milestones would be a good add. The project is not huge and I expect a relatively quick launch on the platform.


    Desirability

    Anything that democratizes AI tooling is currently a valuable step forward. Speeding up processes and lowering thresholds to participate in the cutting edge solutions helps with diversity and equality. There are fundamental limitations regarding LLMs but the solutions behind the proposed project are straightforward, low risk and relatively handy.

     

    Usefulness

    In my opinion that would be a good fit to the SNET platform currently. It certainly has a wide range of potential users and service integrations at least in the mid-term horizon. I don’t really know what the role of it might be over the long-term when new AI paradigms keep emerging but for $35k I find this a relatively good bet.

     

  • 0
    user-icon
    Max1524
    Jun 8, 2024 | 6:20 AM

    Overall

    4

    • Feasibility 4
    • Viability 4
    • Desirabilty 3
    • Usefulness 4
    Specify the learning object

    PromptLab seems like a viable solution to help non-programmers take advantage of short-term learning on large language models (LLM). I encourage this development. But perhaps it is necessary to specify the target audience for PromptLab to provide maximum support (my personal opinion). If we classify each level of learners, I think it will be more accessible, even if it is a superior solution available from the beginning.

  • 0
    user-icon
    Nicolad2008
    Jun 9, 2024 | 1:57 PM

    Overall

    4

    • Feasibility 4
    • Viability 4
    • Desirabilty 4
    • Usefulness 4
    PromptLab

    PromptLab's development team are AI experts working at prestigious companies, with advanced degrees from top universities in the UK and Germany. This expertise ensures the ability to implement projects professionally and effectively. PromptLab solves the problem of accessing learning from few examples for non-expert users, an important step in expanding the applicability of AI. The platform provides an intuitive interface for users to define tasks, number of examples needed, and optimize prompts based on thorough evaluation. Despite its great potential, PromptLab can encounter challenges in evaluating and selecting the optimal prompt for each specific model, especially as the number of models and tasks increases. Additionally, integration and testing with other AI services should also be carefully considered to ensure compatibility and efficiency.

  • 1
    user-icon
    Tu Nguyen
    May 29, 2024 | 3:44 AM

    Overall

    4

    • Feasibility 4
    • Viability 4
    • Desirabilty 4
    • Usefulness 4
    Prompt Generator For Few Shot Learning

    The problem this proposal presents is that few-shot learning, is inaccessible to non-programmers due to the specific formatting required for choosing how to input the examples. Their solution was to create the PrompLab platform, a user-friendly platform designed to streamline the process of interacting with LLMs using zero/few shot learning.
    This proposal should more clearly share members' experiences, in particular, they should share members' social network links. Additionally, they should determine the start and end times of milestones.

  • 1
    user-icon
    CLEMENT
    Jun 3, 2024 | 7:02 PM

    Overall

    4

    • Feasibility 4
    • Viability 4
    • Desirabilty 4
    • Usefulness 4
    Democratizes access to large language models

    I would say that PromptLab has the potential to significantly impact the AI community by democratizing access to state-of-the-art large language models (LLMs) for zero/few-shot learning.

    Additionally, by providing a user-friendly platform for generating effective prompts, PromptLab empowers individuals without coding knowledge to leverage the capabilities of LLMs for various tasks and applications. This project contributes to the democratization of AI technologies by lowering the barrier to entry for non-technical users and enabling them to harness the power of advanced machine learning techniques. Additionally, by facilitating zero/few-shot learning, PromptLab enhances the efficiency and effectiveness of model training and deployment, ultimately driving innovation and advancement within the AI community.

  • 0
    user-icon
    BlackCoffee
    Jun 10, 2024 | 12:10 AM

    Overall

    4

    • Feasibility 4
    • Viability 3
    • Desirabilty 3
    • Usefulness 4
    Should we do a survey?

    The team thought they should create a survey before starting to implement this proposal. The survey asked users' opinions on whether tools like PromptLab are really needed to support training? If we get specific opinions through the survey, I think it will be convenient for the team to deploy in more detail.

  • 0
    user-icon
    TrucTrixie
    Jun 9, 2024 | 1:45 PM

    Overall

    3

    • Feasibility 3
    • Viability 3
    • Desirabilty 3
    • Usefulness 3
    Timeline

    Important milestones with a complete and easy-to-understand description but no specific timeline for implementation. I recommend adding a time element to each milestone. Good luck.

Summary

Overall Community

3.9

from 8 reviews
  • 5
    0
  • 4
    7
  • 3
    1
  • 2
    0
  • 1
    0

Feasibility

3.9

from 8 reviews

Viability

3.8

from 8 reviews

Desirabilty

3.6

from 8 reviews

Usefulness

4

from 8 reviews

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