The Power of Quality Embeddings to Unlock Value From AI | Slides

Here we discuss the significant impact of embedding quality on machine learning model performance, highlighting the challenges of learning good embeddings, particularly for rare or domain-specific words. It also presents various approaches for building high-quality embeddings, including the use of pre-trained embeddings, techniques like Word2Vec or GloVe, fastText for domain-specific words, and fine-tuning embeddings on specific tasks.
SLIDE7
SLIDE7
        
SLIDE8
SLIDE8
        


Topic Description
The Impact of Embedding Quality on Model Performance The quality of embeddings significantly impacts the performance of machine learning models. Embeddings are vector representations of data, and their quality determines how well the model can interpret the data. High-quality embeddings capture the semantic relationships between data points, enabling the model to make accurate predictions. Conversely, poor-quality embeddings can lead to misinterpretations and inaccurate predictions. Therefore, it's crucial to ensure the quality of embeddings to enhance model performance.
Challenges of Learning Good Embeddings Learning good embeddings can be challenging, especially for rare or domain-specific words. These words may not appear frequently in the training data, making it difficult for the model to learn their embeddings. Additionally, domain-specific words may have different meanings in different contexts, adding to the complexity of learning their embeddings. Therefore, it's important to have a robust learning strategy to overcome these challenges and learn good embeddings.
Approaches for Building Good Embeddings There are several approaches for building good embeddings. One approach is to use pre-trained embeddings, which have been trained on large corpora and can capture the semantic relationships between words. Another approach is to use techniques like Word2Vec or GloVe, which learn embeddings by predicting the context of a word. Additionally, for domain-specific words, one can use techniques like fastText, which learns embeddings for subword units, enabling it to handle rare and domain-specific words. Lastly, it's also possible to fine-tune the embeddings on the specific task, allowing the model to learn task-specific representations.



Challenges-in-good-embeddings    Chunking-and-tokenization    Chunking    Dimensionality-reduction-need    Dimensionality-vs-model-perfo    Embeddings-for-question-answer    Ethical-implications-of-using    Impact-of-embedding-dimension    Open-ai-embeddings    Role-of-embeddings-in-various   

Dataknobs Blog

10 Use Cases Built

10 Use Cases Built By Dataknobs

Dataknobs has developed a wide range of products and solutions powered by Generative AI (GenAI), Agent AI, and traditional AI to address diverse industry needs. These solutions span finance, healthcare, real estate, e-commerce, and more. Click on to see in-depth look at these use cases - Stocks Earning Call Analysis, Ecommerce Analysis with GenAI, Financial Planner AI Assistant, Kreatebots, Kreate Websites, Kreate CMS, Travel Agent Website, Real Estate Agent etc.

AI Agent for Business Analysis

Analyze reports, dashboard and determine To-do

DataKnobs has built an AI Agent for structured data analysis that extracts meaningful insights from diverse datasets such as e-commerce metrics, sales/revenue reports, and sports scorecards. The agent ingests structured data from sources like CSV files, SQL databases, and APIs, automatically detecting schemas and relationships while standardizing formats. Using statistical analysis, anomaly detection, and AI-driven forecasting, it identifies trends, correlations, and outliers, providing insights such as sales fluctuations, revenue leaks, and performance metrics.

AI Agent Tutorial

Agent AI Tutorial

Here are slides and AI Agent Tutorial. Agentic AI refers to AI systems that can autonomously perceive, reason, and take actions to achieve specific goals without constant human intervention. These AI agents use techniques like reinforcement learning, planning, and memory to adapt and make decisions in dynamic environments. They are commonly used in automation, robotics, virtual assistants, and decision-making systems.

Build Dataproducts

How Dataknobs help in building data products

Building data products using Generative AI (GenAI) and Agentic AI enhances automation, intelligence, and adaptability in data-driven applications. GenAI can generate structured and unstructured data, automate content creation, enrich datasets, and synthesize insights from large volumes of information. This helps in scenarios such as automated report generation, anomaly detection, and predictive modeling.

KreateHub

Create New knowledge with Prompt library

At its core, KreateHub is designed to enable creation of new data and the generation of insights from existing datasets. It acts as a bridge between raw data and meaningful outcomes, providing the tools necessary for organizations to experiment, analyze, and optimize their data processes.

Build Budget Plan for GenAI

CIO Guide to create GenAI Budget for 2025

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.

RAG For Unstructred and Structred Data

RAG Use Cases and Implementation

Here are several value propositions for Retrieval-Augmented Generation (RAG) across different contexts: Unstructred Data, Structred Data, Guardrails.

Why knobs matter

Knobs are levers using which you manage output

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.

Our Products

KreateBots

Setup chatbots in minutes
  • Pre built front end that you can configure
  • Pre built Admin App to manage chatbot
  • Prompt management UI
  • Personalization app
  • Built in chat history
  • Feedback Loop
  • Available on - GCP,Azure,AWS.
  • Add RAG with using few lines of Code.
  • Add FAQ generation to chatbot

    • KreateWebsites

    LLM and AI based Website generation
  • AI powered websites to domainte search
  • Premium Hosting - Azure, GCP,AWS
  • AI web designer
  • Agent to generate website
  • SEO powered by LLM
  • Content management system for GenAI
  • Buy as Saas Application or managed services
  • Available on Azure Marketplace too.

    • Kreate CMS

    CMS GenAI
  • CMS for GenAI
  • Lineage for GenAI and Human created content
  • Track GenAI and Human Edited content
  • Trace pages that use content
  • Ability to delete GenAI content

    • Generate Slides

    Effortless slides creation
  • Give prompt to generate slides
  • Convert slides into webpages
  • Add SEO to slides webpages

    • Content Compass

    Your story writing is automated
  • Generate articles
  • Generate images
  • Generate related articles and images
  • Get suggestion what to write next