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Implementing Self-Reflective RAG using LangGraph and FAISS

As we build more complex AI applications day by day, there is a need to implement advanced RAG techniques to get better responses from LLMs. We all know RAG is the fundamental piece in building LLM systems. It enables LLMs to generate contextually relevant responses by using external knowledge sources.

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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 However, traditional LLMs rely solely on their pre-trained data, which can lead to outdated or incorrect information.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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Implementation of Contextual Retrieval-Augmented Generation (RAG)

Finding and managing information has come a long way. We're drowning in data these days, and simply finding what we need isn't enough anymore. We need to understand how different pieces of information connect and what they mean together. This has forced us to get creative

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How a leading healthcare provider built an AI-powered drug validation pipeline

Introduction AI copilots assist medical practitioners by providing real-time guidance, supporting diagnosis with data-driven insights and personalized treatment recommendations. Medical practitioners need to look at a lot of details about patients before prescribing the medication—like the patient’s medical history, current symptoms, and how new medications might interact with

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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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How DoorDash Mitigates Hallucinations in its RAG System with a Two-Tier LLM Guardrail: A Hands-On Experiment

In this blog, we’ll explore how DoorDash leverages Retrieval-Augmented Generation (RAG) to address Dashers’ queries using insights from the company’s internal blogs. We’ll conduct practical experiments based on the techniques discussed in DoorDash’s Tech blog, utilizing a curated DoorDash dataset. We will use Athina AI which

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LLMOps Part 3: Deployment

After completing the development and validation phase, model deployment is the next important step in the LLMOps workflow. Successful deployment requires careful consideration of efficiency, scalability, and the specific needs of the deployment environment. Quantization: Optimization Technique for Efficient Deployment of LLMs Quantization is a technique used to reduce LLMs&

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LLMOps Part 2: Workflow and Model Development

Managing large language models (LLMs) through LLMOps workflows requires a structured approach to ensure effective development, deployment, and maintenance. These workflows are divided into several key phases: data collection, model development, deployment, and governance, each crucial to the performance and sustainability of LLM systems. Specific methodologies, tools, and best practices