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7 Essential Generative AI Tools for Building Stand-Out AI Applications

Dive into the technical building blocks of Generative AI, from APIs to LLMOps tools for sustainable app development.
Aug 2023  · 9 min read


Since the start of 2023, we've heard a lot about ChatGPT and other exciting new generative AI tools. ChatGPT took the world by storm as an impressively capable conversational AI application. But it was just the beginning of a new wave of generative AI innovation.

In the months since ChatGPT's launch, we've seen a flood of new startups and companies leveraging similar large language models to create innovative applications. Tools like ChatPDF enable natural conversation with documents, and AutoGPT, which uses agent-based architectures to automate complex tasks. Every day, it seems a new generative AI application launches to solve real-world problems in creative ways.

But how are these powerful systems actually built? And could you create your own customized conversational AI?

In this blog, we will review seven essential generative AI tools for building AI applications. Additionally, we will learn about effective ways of building generative AI applications. Curious as to why AI literacy is so important? Check out our separate guide. 

7 Essential Generative AI Tools

The Generative AI tools provide capabilities like accessing large language models, fine-tuning and training models, performing large language model operations (LLMOps), indexing and storing documents, monitoring, training and serving models, and building web applications.

1. OpenAI

OpenAI's API offers advanced AI models for developers to use. You can generate text with GPT models, find similar text with embeddings, convert speech to text with Whisper, and create images with DALL-E models.

OpenAI's API provides an easy way to access large language and vision models for your application. You don't need to build infrastructure or deploy and monitor your model. With OpenAI's APIs and other developer tools, it's easy for anyone to build an entire AI startup.

You can access OpenAI's generative models through either `curl` or the Python API. The OpenAI API Python cheat sheet provides detailed instructions for accessing all types of models, and our tutorial on using the OpenAI API in Python has more details.

2. Transformers

Both the Transformers Python Library and Hugging Face platforms have played a crucial role in developing an open-source machine learning community.

With Transformers, you can access datasets and models for free within seconds. Additionally, the Transformers Python Library makes it easy to fine-tune large language models on new datasets.

You can upload your model to Hugging Face and use it just like the OpenAI API. Hugging Face also offers enterprise solutions for scalable applications.

Instead of relying on third-party APIs like OpenAI, you can create your own Generative AI model, which allows greater control and security. If you are new to Hugging Face, read our tutorial, An Introduction to Using Transformers and Hugging Face, to learn the basics.

3. LangChain

LangChain is an open-source framework that makes it easy to build applications powered by large language models like GPT-3.5. It offers a modular interface, prompt management, context management, VectorStores, chaining multiple models and tools together, AI agents, and access to top LLMs.

LangChain is an ecosystem that allows users to build AI applications using OpenAPI and other LLMs easily. With just a few lines of code, users can create chatbots, automated AI, and intelligent applications. It is a popular tool in the AI space due to its user-friendliness and fast development capabilities. By following the LangChain tutorial on building LLM Applications, you can understand its key features through example code.

4. Pinecone

Pinecone is a managed vector database optimized for machine learning applications using high-dimensional data. Unlike traditional databases, vector databases like Pinecone are optimized for storing and analyzing complex, multi-dimensional vector representations of data.

Pinecone is a storage system that allows you to integrate PDF documents, Markdown files, and other text data into your language model, enabling personalized answers instead of generalized ones.

Learn to handle high-dimensional and unstructured data efficiently with Pinecone's Mastering Vector Databases tutorial.

Apart from Pinecone, you can also check out ChromaDB. It is a self-managed open-source database that doesn't require signup and works seamlessly with your application.

5. Weights & Biases

Weights & Biases is a platform for machine learning developers to track experiments, visualize results, and optimize the models. It is a lightweight tool for logging metrics, visualizing model training, reproducing experiments, version data, and collaborating with teams.

W&B helps developers build better ML models through experimentation and insights. The platform offers model monitoring and a suite of LLMOps tools built for the development of language applications.

You can use W&B to track Generative AI models' performance during training and in production. As an individual, you can use a cloud server for free or run your server.

If you are new to Weights & Biases, check out the guide Machine Learning Experimentation: An Introduction to Weights & Biases to learn how to structure, log, and analyze your machine learning experiments.

For monitoring Large Language Models in production, WhyLabs has built an open-source toolkit langkit that extracts signals from prompts & responses, ensuring safety & security.

6. BentoML

BentoML is a powerful framework that empowers developers and data scientists to build and deploy AI products quickly and efficiently. With BentoML, the process of integrating pre-trained machine learning models into production becomes seamless, allowing you to deliver value to your users in a matter of minutes.

BentoML offers bentoml/OpenLLM, designed to simplify LLM operations, deployment, fine-tuning, serving, and monitoring. OpenLLM supports many state-of-the-art LLMs and model runtimes like StableLM, Falcon, Dolly, and more. Learn how to build and server your model with OpenLLM in a few minutes by following the Deploy a large language model with OpenLLM and BentoML tutorial.

7. Gradio

Gradio is a powerful tool that has gained significant popularity within AI communities due to its ability to simplify and accelerate the development of machine learning demos. It offers a straightforward and modular approach for building various AI web applications, chatbots, and language applications, making it a go-to solution for many developers.

Similarly, Streamlit is an alternative to Gradio that provides a simplified interface to build web applications for Generative AI.

Before deploying your generative AI model, creating Gradio demos, sharing them with the community, and getting feedback is recommended. It will help you build better applications. Additionally, you can use it as a user interface for quickly testing your models and impressing stakeholders.

To see the tools built upon Generative AI models, refer to The Generative AI Tools Landscape cheat sheet.

Tips for Building Generative AI Applications

Let’s explore some tips for successfully building a cost-effective, secure, compliant, and stable Generative AI application.

1. Start with a clear goal

You need to decide which type of Generative AI application you want to build and what problem you are trying to solve with it. Having a clear goal will save you time and money.

2. Pick the right tools

Make sure you have picked the right third-party APIs, models, integrations, MLOps, monitoring, and automation tools. Choosing the right tools that work for your application is crucial for project success.

3. LLMOps is crucial

You have to follow AI guidelines and monitor and maintain your models in production. By focusing on LLMOps you can reduce operational costs and provide a stable and secure product to end users.

4. Follow security guidelines

Detects and analyzes potential prompt injections, data leakage, and other malicious behaviors. Implement strict input validation and sanitization for user-provided prompts to meet industry standards and avoid legal trouble.

5. Test your models offline

Make sure you test your LLMs offline for accuracy and other performance metrics before deploying them.

6. Start small with limited capabilities.

Instead of building a full-fledged AI platform, start with a simple chatbot feature, then regularly add new capabilities like uploading files, generating images, etc.

7. Model infrastructure

LLMOps can be quite costly, so before choosing cloud services for your application, perform a cost analysis. In most cases, companies lose money because they are unaware of the memory and computation requirements.

8. Monitor your model in production

Check for degraded performance, bias, and malicious use. Have a rollback plan ready.

Read Unlocking Creativity with Generative AI guide to explore art, music, and literature with the help of AI-generated models.


Generative AI represents an exciting new frontier for building innovative applications that were previously out of reach. With the right tools and thoughtful approach, developers now have immense creative potential at their fingertips. Pretrained models like DALL-E 2 and ChatGPT have opened up the ability to generate stunning synthetic images, human-like text, and more.

MLOps frameworks help transition generative models from research to production while monitoring biases and performance, with the expectation of more intuitive interfaces as the technology matures.

Although we must remain cautious about data privacy, security, and societal impacts, generative AI has the potential to enable businesses to deliver truly innovative and valuable experiences, and we have only just begun to explore the possibilities.

Now that you have learned about essential tools, it's time to start building your Generative AI project. Check out this blog for project inspiration: 5 Projects Built with Generative Models and Open Source Tools.

Photo of Abid Ali Awan
Abid Ali Awan

I am a certified data scientist who enjoys building machine learning applications and writing blogs on data science. I am currently focusing on content creation, editing, and working with large language models.


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