Retrieval Augmented Generation (RAG) playbooks - Manoj Jahgirdar

Overview

rag-pipeline

Based on my expertise and experience with Retrieval Augmented Generation (RAG) from past few years, I have created a GitHub repo with different Retrieval-augmented generation (RAG) implementations to share my learnings. The repo consists of the following RAG techniques:

RAG Technique	Simple Definition	When to Use
Simple RAG	Retrieves relevant documents based on the query and uses them to generate an answer	Basic question-answering tasks where context is needed
Simple RAG with Memory	Extends Simple RAG by maintaining context from previous interactions	Conversational AI where continuity between queries is important
Branched RAG	Performs multiple retrieval steps, refining the search based on intermediate results	Complex queries requiring multi-step reasoning or information synthesis
HyDE (Hypothetical Document Embedding)	Generates a hypothetical ideal document before retrieval to improve search relevance	When dealing with queries that might not have exact matches in the knowledge base
Adaptive RAG	Dynamically adjusts retrieval and generation strategies based on the query type or difficulty	Varied query types or when dealing with a diverse knowledge base
Corrective RAG (CRAG)	Iteratively refines generated responses by fact-checking against retrieved information	High-stakes scenarios requiring increased accuracy and fact verification
Self-RAG	The model critiques and improves its own responses using self-reflection and retrieval	Tasks requiring high accuracy and when there’s time for multiple refinement steps
Agentic RAG	Combines RAG with agentic behavior, allowing for more complex, multi-step problem-solving	Complex tasks requiring planning, decision-making, and external tool use

GitHub Link

Repository

Skills picked up

Embeddings
Sentence Transformer
Vector stores
Branched RAG