Data Governance for AI Assistant using Large Language ModelData governance for an AI Assistant built using a Large Language Model (LLM) is crucial to ensure the responsible and ethical use of data. It involves establishing policies, procedures, and controls to manage data quality, security, privacy, and compliance throughout the AI system's lifecycle. New Factors for LLM Governance
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Enterprises are most successful when they treat data like a product. It enable to use data in multiple use cases. However data product should be designed differently compared to software product.
Generative AI has enabled many transformative scenarios. We combine generative AI, AI, automation, web scraping, ingesting dataset to build new data products. We have expertise in generative AI, but for business benefit we define our goal to build data product in data centric manner. Our Product KREATE enable creation of data, user interface, AI assistant. Click to see it in action.
To build a commercial data product, create a base data product. Then add extension to these data product by adding various types of transformation. However it lead to complexity as you have to manage Data Lineage. Use knobs for lineage and extensibility
CIOs and CTOs can apply GenAI in IT Systems. The guide here describe scenarios and solutions for IT system, tech stack, GenAI cost and how to allocate budget. Once CIO and CTO can apply this to IT system, it can be extended for business use cases across company.
KREATE empowers you to create things - Dataset, Articles, Presentations, Proposals, Web design, Websites and AI Assistants Kreate is a platform inclide set of tools that ignite your creatviity and revolutionize the way you work. KReatePro is enterprise version.
KONTROLS enable adding guardrails, lineage, audit trails and governance. KOntrols recogizes that different use cases for Gen AI and AI have varying levels of control requirements. Kontrols provide structure to select right controls.
Well defined tunable paramters for LLM API, LLM fine tuning , Vector DB. These parameters enable faster experimentation and diagosis for every state of GenAI development - chunking, embedding, upsert into vector DB, retrievel, generation and creating responses for AI Asistant.
Create articles for Blogs, Websites, Social Media posts. Write set of articles together such as chapters of book, or complete book by giving list of topics and Kreate will generate all articles.
Design impactful presentation by giving prmpt. Convert your text and image content into presentations to win customers. Search in your knowledbe base of presentations and create presentations or different industry. Publish these presentation with one click. Generate SEO for public presentations to index and get traffic.
AI powered website generation engine. It empower user to refresh website daily. Kreate Website AI agent does work of reading conent, website builder, SEO, create light weight images, create meta data, publish website, submit to search engine, generate sitemap and test websites.
Set up AI Assistant that give personized responss to your customers in minutes. Add RAG to AI assistant with minimal code- implement vector DB, create chunks to get contextual answer from your knowlebase. Build quality dataset with us for fine tuning and training a cusom LLM.
AI agent independently chooses the best actions it needs to perform to achieve their goals. AI agents make rational decisions based on their perceptions and data to produce optimal performance and results. Here are features of AI Agent, Types and Design patterns
As per HBR Data product require validation of both 1. whether algorithm work 2. whether user like it. Builders of data product need to balance between investing in data-building and experimenting. Our product KREATE focus on building dataset and apps , ABExperiment focus on ab testing. Both are designed to meet data product development lifecycle
In complex problems you have to run hundreds of experiments. Plurality of method require in machine learning is extremely high. With Dataknobs approach, you can experiment thru knobs.
Here are several value propositions for Retrieval-Augmented Generation (RAG) across different contexts: Unstructred Data, Structred Data, Guardrails.
See Drivetrain appproach for building data product, AI product. It has 4 steps and levers are key to success. Knobs are abstract mechanism on input that you can control.