Prasad Mahamulkar

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Building Adaptive RAG using LangChain and LangGraph

The rise of Large Language Models (LLMs) has significantly improved AI-driven question-answering (QA) systems. However, traditional LLMs rely solely on their pre-trained data, which can lead to outdated or incorrect information. To address this limitation, Retrieval-Augmented Generation (RAG) is used. RAG integrates external knowledge sources, enhancing both accuracy and reliability.

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Implementing RAG using Hypothetical Document Embeddings (HyDE)

Retrieval-augmented generation (RAG) is a powerful technique in information retrieval where an external knowledge base is leveraged to improve the quality and accuracy of responses. One of the challenges in traditional RAGl systems is the dependence on explicit query-document similarity, which often requires labeled relevance data. Hypothetical Document Embeddings (HyDE)

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Agentic RAG using DeepSeek R1, Qdrant, and Langchain

Over the past decade, artificial intelligence has grown rapidly, with Large Language Models (LLMs) changing the way we interact with machines. While these models are powerful, they depend on pre-existing data and struggle with real-time updates or complex reasoning. Retrieval-Augmented Generation (RAG) was developed to solve this by integrating external

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Advanced RAG Implementation using Hybrid Search

Retrieval-augmented generation (RAG) has transformed the way large language models (LLMs) generate responses by integrating external data. Hybrid RAG, an advanced approach, combines vector similarity search with traditional methods like BM25 and keyword search, enabling more robust and flexible information retrieval. Hybrid RAG is an advanced retrieval technique that merges

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Implementation of RAG Fusion using LangChain, Qdrant, and Athina

Retrieval-augmented generation (RAG) improves large language models (LLMs) by integrating external data, enhancing the relevance and accuracy of outputs. Instead of relying solely on pre-trained knowledge, RAG fetches and uses information from external sources like vector databases, making it ideal for domain-specific or up-to-date tasks. However, traditional RAG has limitations,

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Building RAG with Unstructured Data

Retrieval-augmented generation (RAG) improves the large language model's (LLM) response by using external data to enhance the relevance and accuracy of their outputs. Instead of relying only on pre-trained knowledge, RAG allows models to fetch and integrate information from external sources, such as vector databases. This approach is

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Evaluating a New Claude Sonnet 3.5 Model in Athina AI

The new Claude Sonnet 3.5 model demonstrates significant improvements, particularly in mathematical reasoning, coding, visual understanding, and agentic tool use. These enhancements make it better at handling complex problems and more versatile across various tasks. Users can expect improved accuracy with this version. In this tutorial, we will guide

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Introduction to RAG Evaluation Metrics

Retrieval-Augmented Generation (RAG) is becoming popular for building applications with large language models (LLMs). RAG improves generative models by using external information to make their responses more accurate and context-aware. This guide explains how basic RAG works and how to evaluate the RAG pipeline with specific metrics, using simple steps