GenerativeAI AppStore – Enterprise Readiness Unleashed

I’m thrilled to share a groundbreaking development at TRUGlobal in the world of #artificialintelligence – the #GenerativeAI AppStore. 

AI has become an integral part of every industry, offering countless possibilities for innovation. Generative AI, a subset of AI that focuses on creating content such as text, images, and more, has opened up new horizons for creativity and problem-solving. But….
1. How do I get my own Generative AI for specific enterprise needs?
2. How can I customize a pre-trained Generative AI GPT for my purpose?
3. Can a Generative AI solution connect to my data, and work?
4. How much time to invest before it can be made productive for me?

GenerativeAI AppStore at TRUGlobal, answers these questions broadly –

• It’s a one-stop destination for discovering, accessing, and harnessing the power of cutting-edge generative AI applications.
• It’s a hub for developers, businesses, and AI enthusiasts to explore a plethora of apps that leverage the capabilities of generative AI, from content creation to data analysis, solving business problems, and beyond.
• It’s where the magic happens, as innovators and creators from diverse backgrounds come together to redefine what’s possible with the help of AI.
• Completely open source based, not OpenAI / ChatGPT, and can be fine-tuned to be deployed on-prem as well, making the solution less compute intensive.

Power of 3:
TRUGlobal GenerativeAI App Store has a 3-tier architecture and offers the 3-3-3:

• 3 days to deploy and configure, as-is, within an enterprise
• 3 weeks of prompt engineering, service-model-based
• 3 months to fine-tune the LLM, service-model-based

Front end Layer:
The front end is built on react.js and has modules for user onboarding, with an in-built integration component with an enterprise’s security directory service e.g. ActiveDirectory.
The front end also provides a user prompt section to interact with GenerativeAI AppStore seamlessly and a configuration module for a user to set up the enterprise data store.

Integration Layer:
The integration layer is the brain of the solution that connects the front end with the backend orchestrated by open-source FlaskAPI.

DBconnector API:
The AppStore provides a plethora of connectivity options to the backend from CSV to popular DBs to NoSQL data stores.

LangChain API:
It offers a chat interface to call the model APIs into your application and create a question/answer pipeline that answers users’ queries based on given context or input documents. It basically performs a vectorized search to find the most similar answer to the question.
Tasks like prompt chaining, logging, callbacks, persistent memory, and efficient connections to multiple data sources come standard out of the box with LangChain.
Langchain also provides a model-agnostic toolset that enables them to explore multiple LLM offerings and test what works best for their use cases. The best part is that it can do this in a single interface instead of the need to scale the size of a codebase.

Prompt Engineering Module:
Prompt engineering component unlocks the real value of LLMs and the key to making the solution industrial and enterprise-ready.
As an analogy, prompt engineering is like a restaurant order:

1. Customer (User): The customer represents the user of an AI system. They have a specific order in mind, which in AI terms, would be their desired output or information.
2. Menu (AI Model): The menu is like the AI model. It contains a wide range of dishes (capabilities) that the restaurant (AI model) can prepare. However, the menu is not exhaustive, and it’s up to the customer to formulate their order (prompt) based on what’s available.
3. Order (Prompt): The customer must carefully craft their order (prompt) to ensure they receive the exact meal they desire. This includes specifying the dish, its ingredients, cooking preferences, and any dietary restrictions. The prompt is analogous to the user’s input or query to an AI model.
4. Chef (AI System): The chef in the kitchen is like the AI system that receives and processes the order (prompt). The chef follows the instructions given in order to prepare the dish (generate the output).
5. Dish (Output): The dish served to the customer is the output generated by the AI system based on the order (prompt). If the order is clear and precise, the customer receives the desired dish. If the order is vague or unclear, the customer may not get what they wanted.
6. Feedback Loop (Iterative Process): Just as in prompt engineering, if the customer is not satisfied with the dish, they may provide feedback, allowing the chef to learn and improve for the next order. In prompt engineering, user feedback is crucial for refining prompts and training AI models.

LLM FineTune Module:
This module has the real innovation in the GenerativeAI space. The TRUGlobal GenerativeAI AppStore provides the functionality to fine-tune the ‘working’ LLM after robust prompt engineering to ‘scale down’ the model. This allows specificity of the problem being solved and reduction in almost 70% of compute capacity needs in real-time.

Backend Layer:
Two main components form the backend layer:
AppStore – The repository of open-source fine-tuned GPT(Generative Pre-trained Transformer) models. Currently, we use decoder-heavy models to accelerate and scale the solution.
Data Tier – The data tier is within the enterprise, though for chat history, we use MySQL which is offered as an optional component to enterprises.

Enterprise Usage:
The SaaS Apps in the GenerativeAI AppStore are modular in nature and the components are loosely coupled, making it easy and transparent for enterprises to use as per their need.
#GenerativeAI #AIInnovation #AppStore #AIApplications #CreativityUnleashed