The LLM Augmentation Journey With RAG

The LLM Augmentation Journey With RAG | Slides

Summary: The article discusses the multi-stage process of LLM Retrieval Augment Generation, outlining the stages of Pre-Retrieval, Retrieval, Post-Retrieval, and Generation, each with specific sub-stages aimed at enhancing information retrieval and generation for improved user experience.


SLIDE1	SLIDE2	SLIDE3
SLIDE4	SLIDE5	SLIDE6
SLIDE7	SLIDE8	SLIDE9
SLIDE10	SLIDE11	SLIDE12
SLIDE13	SLIDE14	SLIDE15
SLIDE16	SLIDE17	SLIDE18
SLIDE19	SLIDE20	SLIDE21
SLIDE22	SLIDE23

RAG (Retrieval Augmentation Generation)

RAG, short for Retrieval Augmentation Generation, is a powerful model that combines the capabilities of retrieval, augmentation, and generation in natural language processing tasks. It is designed to enhance the performance of question-answering systems and text generation models by integrating these three key components.

How RAG Works:

RAG operates in three main stages:

Stage	Description
1. Retrieval	RAG first retrieves relevant information from a large knowledge source, such as a text corpus or a database, based on the input query or context. This retrieval step helps in narrowing down the search space and focusing on the most relevant data.
2. Augmentation	After retrieving the initial information, RAG augments this data by adding more context or details to improve the understanding of the content. This augmentation step enriches the retrieved information and provides a more comprehensive view of the topic.
3. Generation	Finally, RAG generates a coherent and informative response or output based on the retrieved and augmented data. This generation step ensures that the model produces accurate and contextually relevant answers to queries or prompts.

Role of Vector DB:

Vector DB plays a crucial role in the functioning of RAG by providing a structured and efficient way to store and retrieve vector representations of text data. These vector embeddings capture the semantic meaning and relationships between words, sentences, or documents, enabling RAG to perform similarity-based searches and context-aware retrievals.

By leveraging Vector DB, RAG can quickly access and manipulate vectorized representations of textual information, facilitating the retrieval and augmentation processes. This integration enhances the model's ability to understand and generate natural language responses with improved accuracy and relevance.

LLM Retrieval Augment Generation

LLM Retrieval Augment Generation is a multi-stage process that involves various sub-stages to enhance the retrieval and generation of information. Below are the four main stages along with their sub-stages:

Stage	Sub-Stages
Pre-Retrieval	Indexing, Query Manipulation, Data Modification
Retrieval	Search, Ranking
Post-Retrieval	Re-Ranking, Filtering
Generation	Enhancing, Customization, Content Synthesis

Pre-Retrieval

In the Pre-Retrieval stage, the focus is on preparing the data for efficient retrieval. This involves indexing the data, manipulating queries to improve search results, and modifying the data structure for better organization.

Retrieval

The Retrieval stage involves the actual search process and ranking of results based on relevance. Search algorithms are applied to retrieve information, and ranking algorithms determine the order in which results are presented to the user.

Post-Retrieval

After retrieving the initial results, the Post-Retrieval stage focuses on refining the results further. This may involve re-ranking the results based on additional criteria and applying filters to narrow down the information to the most relevant.

Generation

In the Generation stage, the emphasis is on enhancing the retrieved information, customizing it to fit specific user needs, and synthesizing content to provide a more comprehensive output. This stage aims to generate augmented content that adds value to the retrieved information.

Rag-for-structured-and-unstru Rag-for-strucutred-data Sql-stats-genai-rag-methods-f

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

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

Fractional CTO for generative AI and Data Products

Hire expertise without commitment

Deliver E2E use case

Generative AI expertise

Machine Learning expertise

Data product building expertise

Cloud - AWS, GCP,Azure

The LLM Augmentation Journey With RAG | Slides