> ## Documentation Index > Fetch the complete documentation index at: https://manojjahgirdar.com/llms.txt > Use this file to discover all available pages before exploring further. # Retrieval Augmented Generation (RAG) playbooks > A comprehensive repo with different types of retrieval augmented generation (RAG) techniques. ## 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](https://github.com/manojjahgirdar/types-of-retrieval-augmented-generation) ## Skills picked up * Embeddings * Sentence Transformer * Vector stores * Branched RAG