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Top tools for Prompt Engineering?

Updated: Feb 6


The process of LLM Prompt Engineering

The advance of artificial intelligence (AI) and large language models (LLMs) has significantly altered a variety of sectors, laying the foundation for the creation of innovative applications that emulate human-like textual understanding and generation capabilities. As the world delves deeper into an era defined by AI, the significance of Prompt Engineering is growing. It is often the first step towards extracting the vast potential of LLMs by curating specific prompts tailored to unique business needs. This paves the way for the creation of custom AI solutions, making AI more useful and available to a larger demographic.


However, despite its importance in creating high-quality content with LLMs, Prompt Engineering can be an iterative and challenging task. It involves a number of processes, including data preparation, the crafting of custom prompts, the execution of these prompts through the LLM API, and the refinement of the generated content. These steps converge to form a flow that users progressively optimize to perfect their prompts and generate the most suitable content for their specific business context.


The challenges that arise in Prompt Engineering can be categorized into three key areas:

  1. Design and development: This requires users to understand LLMs, experiment with various prompts, and employ complex logic and control flow to create effective prompts. Additionally, users may encounter a cold start problem, with no previous examples or knowledge to guide them.

  2. Evaluation and refinement: Ensuring that the outputs are consistent, useful, unbiased, and harmless is crucial here. Users must also define and measure prompt quality and effectiveness using standard metrics.

  3. Optimization and production: This involves monitoring and troubleshooting prompt issues, improving prompt variants, optimizing prompt length without compromising performance, handling token limitations, and protecting prompts from injection attacks.


Tools for efficient prompt engineering

In response to these challenges, an innovative solution termed 'prompt flow' has been developed by various vendors. These tools expedite and simplify the development, evaluation, and continuous integration and deployment (CI/CD) of prompt engineering projects. They equip data scientists and LLM application developers with an interactive platform that merges natural language prompts, templating language, in-built tools, and Python code. These tools expertly guide users through the journey from initial ideation and experimentation, culminating in the creation of production-ready applications powered by large language models (LLMs).


The best tools for prompt engineering are:



The Open Source: TensorOps' LLMStudio



Working with LLMstudio


LLMStudio by TensorOps is a pioneering platform tailored to simplify the process of working with Language Learning Models (LLMs). It stands as a pivotal solution for developers and organizations aiming to leverage LLMs in their workflows. This platform demystifies the complexity of integrating and utilizing LLMs, presenting a bevy of features that foster a more efficient and user-friendly experience.


The main offerings of LLMStudio include:

  • Graphical studio - web based UI for interactive prompt design, history visualization, sending prompt from the UI to different vendors etc.

  • LLM Gateway - centralized access to different LLMs, including support for OpenAI, Azure OpenAI, AWS Bedrock and custom connector to any LLM that can be implemented.

  • Prompt storage - Postgres database to store the history of the previous calls to LLMs

  • Python SDK and REST API - allowing to integrate LLMstudio gateway into any backend code, whether as python client or RESTful implementation.


To begin with LLMStudio, users can engage with a straightforward setup by just pip installing the package, either as part of their local IDE, or in the production system with the following command:


pip install llmstudio
llmstudio server --ui

LLMStudio places a strong emphasis on community and extensibility, encouraging users to contribute to the platform's growth and expansion. It supports various integrations and seeks community feedback to evolve its features continuously.


We at TensorOps really believe in our open source project. Like how Jupyter helped data science and machine learning grow, we think that tools for building LLM apps should also be open source. Our tools are made by developers for developers. We love how open source work helps everyone create and share together.


What tool do you use for Prompt Engineering?

  • LLMStudio

  • Cloud vendor tools (GCP, Azure etc)

  • Langsmith

  • Other (comment below)

You can vote for more than one answer.


Azure PromptFlow

Currently, Azure stands out as the most sophisticated platform in this stack. Azure's PromptFlow, a ground-breaking tool designed to streamline the design, evaluation, and deployment of prompt engineering projects for Large Language Models (LLMs). It offers an interactive environment for data scientists and developers working with LLM applications, effectively integrating natural language prompts, templating language, inbuilt tools, and Python code.

Azure's PromptFlow boasts several key features, including:

  • Design and development: The platform offers a notebook-style programming interface, a Directed Acyclic Graph (DAG) view, and a chat bot experience, thereby allowing the creation of versatile workflows. It guides users through each step, from crafting and refining prompt variants to testing, evaluating, and finally deploying the flow.

  • Evaluation and optimization: With PromptFlow, users can effortlessly create, run, evaluate, and compare numerous prompt variants, thereby promoting the exploration and enhancement of prompts. Custom metrics and incorporated evaluation flows enable users to assess the quality and performance of their prompts.

  • Production readiness: Upon exhaustive evaluation, PromptFlow presents a single-click deployment solution for enterprise-grade applications. Moreover, it continuously monitors the deployed applications to guarantee stability and consistent performance.


Source: https://learn.microsoft.com/en-us/azure/machine-learning/prompt-flow/get-started-prompt-flow?view=azureml-api-2

LangSmith by LangChain

LangSmith is a platform designed to simplify debugging, testing, evaluating, and monitoring large language model (LLM) applications. It aims to bridge the gap between prototypes and production-ready applications.

Key Features of LangSmith include:

  1. Debugging: It offers full visibility into the sequence of model inputs and outputs, enabling quick identification and resolution of errors.

  2. Testing: It provides a simple way to create and manage test datasets. Developers can evaluate the effects of changes in their application by running tests over these datasets.

  3. Evaluating: LangSmith integrates with evaluation modules, employing both heuristic logic and LLMs themselves to evaluate the correctness of an answer.

  4. Monitoring: It offers tools to monitor system-level performance (such as latency and cost), and track user interactions, helping developers optimize their applications based on feedback and performance metrics.

  5. Unified Platform: LangSmith serves as an integrated hub for all stages of LLM application development, streamlining the development process.

Furthermore, LangSmith supports data export in formats compatible with OpenAI evaluations and analytics engines, promoting easy fine-tuning and analysis of models. It's currently in closed beta.


Helicone is an innovative observability platform specifically designed for Language Learning Models (LLMs), offering a suite of tools that enhance the user experience for developers and teams working with OpenAI's APIs. This open-source platform is a game-changer in managing and optimizing interactions with LLMs, providing a range of features that streamline the development process.

Key features of Helicone include:

  • A user-friendly UI that logs all OpenAI requests, allowing for easy tracking and management.

  • Caching capabilities, custom rate limits, and automatic retries to ensure efficient use of resources.

  • Detailed tracking of costs and latencies, segmented by users and other custom properties.

  • A playground within every log for prompt and chat conversation iteration directly in the UI.

  • Collaboration and result-sharing tools for better teamwork.

  • Upcoming APIs for logging feedback and evaluating results to further improve LLM integration.

Getting started with Helicone is quick and straightforward. Users can sign up to receive an API key, install the Helicone package, and immediately begin making enhanced requests to the OpenAI API with additional features like caching and custom rate limits.

Helicone also promises ease of local setup, with a simple installation process for its various components such as the frontend, proxy worker, application database, and analytics database. The platform's cloud offering, deployed on Cloudflare, guarantees minimal latency for API requests, enhancing overall performance.


Jupyter Notebooks

Jupyter Notebooks have become an indispensable tool for developers, particularly when dealing with complex data analysis and machine learning tasks. As a complete Python interpreter, they offer the flexibility to connect with various data sources, integrate seamlessly with existing codebases, and enable real-time code execution with immediate visual feedback. This interactive environment encourages exploratory analysis and iterative coding, making it an ideal platform for developing, documenting, and executing data-intensive workflows.



However, while Jupyter Notebooks excel in interactive development and data manipulation, they require external libraries to enhance their capabilities in areas such as experiment tracking and model versioning. This is where tools like LLMstudio and MLflow come into play. They complement Jupyter Notebooks by providing structured environments for logging experiments, tracking model evolution, and managing the machine learning lifecycle. By integrating with these libraries, developers can maintain a comprehensive record of their models' performances and iterations, which is crucial for reproducibility and collaborative development in professional data science and machine learning projects. The synergy between Jupyter Notebooks and these tracking libraries forms a powerful combination, allowing developers to not only write and test their code efficiently but also to maintain a high level of organization and oversight over their machine learning experiments.


Other Notable tools


Google MakerSuite

Google's MakerSuite is a user-centric platform designed to facilitate the easy prototyping of generative AI ideas. The platform is engineered in such a way that it does not necessitate extensive machine learning expertise, thus making it accessible to a broader audience.

MakerSuite's primary features include:

  • Prototype building and sharing: After preparing the model, you can save and share your prototype with your entire team.

  • Scaling your prototype to production: MakerSuite enables you to transform your prompts into code that is ready for production, compatible with development environments such as Colab, in just one click.

  • Access to the PaLM API: MakerSuite offers a simplified user interface for prototyping with the PaLM API and accessing your API key.

In addition, the platform features a prompt gallery to inspire users and provide examples, thus assisting in the developmental process. Interested users can join a waitlist for access.


Source: https://developers.generativeai.google/products/makersuite


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