Vellum AI isn’t a single tool or software – it’s actually a suite of different products and tools that help with various AI-related tasks like prompt engineering, model versioning, LLM applications, semantic search, and performance monitoring. It’s a platform that is designed to help smaller companies and developers take their LLMs from prototypes to full-scale production. It’s easy to use and streamlines various components of launching an LLM (large language model) – building, deploying, and refining AI-powered apps. As we previously mentioned, its main audience is tech startups, AI engineers, and companies adopting generative AI software and technology.
As far as competition goes, OpenAI’s API and Hugging Face are the main ones in the LLM field. Both of these competitors offer powerful community resources alongside their API; Vellum makes a name for itself through workflow automation and low-code interfaces as its focus. It’s more accessible to non-technical users (which seems to be in high demand nowadays) and offers much better support for multi-step applications that tend to be more complex, making it a strong LangChain alternative. Hugging Face does beat Vellum in one area, model experimentation and community support, but Vellum has its versioning and prompt chaining feature, which gives it an edge where maintaining, LLM evaluation, and iterating on models is important.
Key Features of Vellum AI
The key features of Vellum AI can be broken down into the products that it offers. These are designed to simplify and enhance various parts of the development process for LLM-powered apps.
Prompt Engineering
As with many other tools of its type, Vellum mainly started off with Prompt Engineering when ChatGPT first appeared and took the world by storm. Prompt engineering is a term that was popularized as the user base for ChatGPT grew and the need for better and more nuanced prompts and texts arose. Vellum’s Prompt Engineering helps users and developers design, iterate, and test various prompts effectively, but it’s crucial to consider LLM hallucination during the testing phase, where the model might generate misleading or incorrect outputs.
Semantic Search
“That’s just semantics” is a phrase you’ve probably heard before. You might have used it in an argument yourself. This is because semantics is the study of meaning in language. When someone uses it in an argument, they’re arguing that the disagreement isn’t about the core or substance of the argument; it’s about differences in the meaning of the language used to talk about it. This is why the Semantic Search feature of Vellum AI is important. It enhances the model’s ability to understand context-aware queries. It allows the LLM to understand the meaning behind the question/query. This kind of function is crucial in knowledge database LLMs as well as applications like support chatbot LLM apps.
Model Versioning and Chaining
Explaining model versioning is pretty simple – it’s been a standard practice in programming/software engineering for a long time, and it’s no different with LLMs. Versioning allows devs to keep track of different versions of their models. The ability to roll back to a prior version if something goes wrong or to iterate on previous versions for constant improvement ensures that devs can experiment safely without affecting live apps.
As far as Model Chaining goes, it’s self-explanatory. It’s the ability to chain up sequences for multiple models to execute tasks in succession or in a certain order. One model’s output can become the input for the next one, etc. Running models in sequence allows them to answer complex multi-part queries.
Getting started with Vellum AI
Starting up Vellum’s AI tools is as simple as registering for a website. Create an account, log in, and then purchase whatever plan you’re comfortable with. You can book demos for each of their products, and if you decide it’s worth it, you can start exploring every tool that they offer.
To keep it simple, here are the steps to take:
- Go to Vellum AI and sign up.
- Verify your account and log in to the dashboard.
- Create a New Project from the dashboard.
- Select an LLM Provider from the list.
- Use their prompt engineering tool to design and test prompts.
- Implement Semantic Search (Optional).
- Deploy your model after testing by using Vellum Deployments to launch your model into production.
