Generative AI Applications Archives

Accelerating Enterprise AI Development with Retrieval-augmented Generation

by ZBrain | Oct 11, 2024 | Generative AI Applications, Generative AI Use Cases

← All Insights Accelerating Enterprise AI Development with Retrieval-augmented Generation Listen to the article Your browser does not support the audio element. Enterprises are increasingly leveraging AI to foster innovation, improve decision-making, and optimize...

What is ZBrain and how to use it?

by ZBrain | Sep 11, 2024 | Generative AI Applications

← All Insights What is ZBrain and how to use it? Listen to the article Your browser does not support the audio element. The growing adoption of advanced Large Language Models (LLMs) is driving businesses across industries to explore innovative ways to integrate them...

Fine-tuning pre-trained models for Generative AI applications

by ZBrain | May 10, 2023 | Fine-tuning, Generative AI Applications

← All Insights Generative AI has been gaining huge traction recently thanks to its ability to autonomously generate high-quality text, images, audio and other forms of content. It has various applications in different domains, from content creation and marketing to...

Generative AI Use Cases and Applications

by ZBrain | May 10, 2023 | Generative AI Applications, Generative AI Use Cases

← All Insights The advent of Artificial Intelligence (AI) has significantly impacted the way businesses operate and manage daily workflows. The emergence of diverse AI applications and tools has enabled businesses to make wiser decisions and automate repetitive tasks,...

ZBrain credits and their usage

A credit is a unit of usage on ZBrain. Credits are consumed whenever you perform actions such as embedding documents or querying an app. Here’s how credits are utilized:

Document Embedding:

When you upload a document, it is processed and embedded to create a searchable vector representation. This step consumes credits based on the size of the document and the embedding model used.

Input Processing:

When you ask a question, the relevant context is retrieved from the embeddings and processed by the Large Language Model (LLM). This step incurs a credit cost based on the model and the number of input tokens.

Generating Output

The LLM then generates a response based on the processed input, consuming additional credits based on the number of output tokens.

The cost in credits for various models and processes is detailed below:

Models and Credit Consumption:

Model	Input Cost	Output Cost
GPT-4o	2,500 credits / 1M tokens	7,500 credits / 1M tokens
GPT-4	15,000 credits / 1M tokens	30,000 credits / 1M tokens
GPT-4-32k	30,000 credits / 1M tokens	60,000 credits / 1M tokens
GPT-3.5 Turbo	250 credits / 1M tokens	750 credits / 1M tokens

Embedding Models:

Model	Input Cost
text-embedding-3-small	20 credits / 1M tokens
text-embedding-3-large	130 credits / 1M tokens
ada v2	100 credits / 1M tokens

When you create a knowledge base or query an app, credits are deducted based on the embedding, input, and output token usage. This ensures you only pay for what you use while leveraging the full power of advanced AI models.

Accelerating Enterprise AI Development with Retrieval-augmented Generation

What is ZBrain and how to use it?

Fine-tuning pre-trained models for Generative AI applications

Generative AI Use Cases and Applications

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