Akshay Kumar

@akshaydev07

Developer, building usefull things for the world

Joined January 2025

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Akshay Kumar's blogs

Advanced RAG: Query Expansionrag-query-transformation-fan-out.hashnode.dev4 posts

Decoding AI Jargons with Chaiintrotoai.hashnode.dev2 posts

Refresh Token Access Tokenrefresh-access-token.hashnode.dev1 post

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Recently published

AKAkshay Kumarrag-query-transformation-fan-out.hashnode.devJun 15, 2025 · 4 min read

HyDE in RAG: Elevating Retrieval with Hypothetical Document Embeddings 🚀

🔍 Introduction Retrieval-Augmented Generation (RAG) has transformed how Large Language Models (LLMs) interact with external knowledge by enabling them to “look things up” before answering. However, one key challenge remains — retrieving the most rel...

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AKAkshay Kumarrag-query-transformation-fan-out.hashnode.devJun 15, 2025 · 4 min read

🧩 Unlocking Smarter Retrieval: Query Decomposition in RAG

🚀 Introduction Retrieval-Augmented Generation (RAG) has transformed how Large Language Models (LLMs) access and use external knowledge. But a single query — especially if it's vague or multi-step — often isn't enough to retrieve the most relevant in...

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AKAkshay Kumarrag-query-transformation-fan-out.hashnode.devJun 14, 2025 · 3 min read

Optimizing RAG Retrieval with Rank Fusion and RRF Methods

Retrieval-Augmented Generation (RAG) systems have become a go-to approach for combining LLMs with real-world data. But as we continue to improve retrieval strategies, one powerful technique has started gaining more traction: Reciprocate Rank Fusion (...

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AKAkshay Kumarrag-query-transformation-fan-out.hashnode.devJun 14, 2025 · 4 min read

Supercharging RAG with Query Transformation and Parallel Query (Fan Out) Retrieval

Retrieval-Augmented Generation (RAG) has become a powerful strategy to enhance the capabilities of large language models (LLMs). But while RAG solves a lot of problems around hallucination and grounding, it's not without its own limitations. In this ...

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AKAkshay Kumarintrotoai.hashnode.devJun 13, 2025 · 7 min read

What is Retrieval-Augmented Generation (RAG) and How Does It Function?

✨ What is RAG Explain the concept simply: RAG is an architecture that combines retrieval (searching for information) with generation (creating responses) in large language models (LLMs). It lets models access external knowledge to answer questions m...

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Akshay Kumar

About

Available for

Akshay Kumar's blogs

Recently published

HyDE in RAG: Elevating Retrieval with Hypothetical Document Embeddings 🚀

🧩 Unlocking Smarter Retrieval: Query Decomposition in RAG

Optimizing RAG Retrieval with Rank Fusion and RRF Methods

Supercharging RAG with Query Transformation and Parallel Query (Fan Out) Retrieval

What is Retrieval-Augmented Generation (RAG) and How Does It Function?

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Akshay Kumar

About

Available for

Akshay Kumar's blogs

Recently published

HyDE in RAG: Elevating Retrieval with Hypothetical Document Embeddings 🚀

🧩 Unlocking Smarter Retrieval: Query Decomposition in RAG

Optimizing RAG Retrieval with Rank Fusion and RRF Methods

Supercharging RAG with Query Transformation and Parallel Query (Fan Out) Retrieval

What is Retrieval-Augmented Generation (RAG) and How Does It Function?