Slide 1 of 9
Slide 1 - Retrieval-Augmented Generation Pipeline
Retrieval-Augmented Generation Pipeline
One Slide Per Subprocess: Formatting, Chunking, Embedding, Retrieval, Reranking, LLM Processing
---
Photo by İsmail Enes Ayhan on Unsplash
Retrieval-Augmented Generation Pipeline: Key Subprocesses Explained
Generated from prompt:
I need a RAG pipeline presentation.one slide for one sub process: data source formatting, chunking, embedding, retrieval, reranking, LLM processing
This presentation provides a detailed walkthrough of the Retrieval-Augmented Generation (RAG) pipeline, with one slide dedicated to each core subprocess: Data Source Formatting, Chunking, Embedding, Retrieval, Reranking, and LLM Processing. It covers normalization of diverse inputs, semantic chunking strategies, vector embeddings and storage, similarity-based retrieval, relevance reranking, and prompt construction for grounded LLM responses. Key benefits include reduced hallucinations, scalable external knowledge integration, and context-aware generation.
May 8, 20269 slides
Slide 2 of 9
Slide 2 - RAG Pipeline Agenda
- Data Source Formatting
- Chunking
- Embedding
- Retrieval
- Reranking
- LLM Processing
---
Photo by Beatriz Cattel on Unsplash
Slide 3 of 9
Slide 3 - Data Source Formatting
- Normalize various inputs (PDFs, docs, web, DBs)
- Handle text extraction and cleaning
- Ensure consistent structure for downstream processing
- Remove noise, duplicates, metadata extraction
---
Photo by Deng Xiang on Unsplash
Slide 4 of 9
Slide 4 - Chunking
- Split large documents into smaller, overlapping chunks (e.g., 512 tokens)
- Methods: fixed-size, sentence-based, semantic chunking
- Preserve context with overlap (20-30%)
- Optimize for embedding model limits
Slide 5 of 9
Slide 5 - Embedding
| Step | Action |
|---|---|
| 1 | Convert text chunks to dense vectors using embedding model (e.g., OpenAI text-embedding-ada-002) |
| 2 | Capture semantic meaning in high-dimensional space |
| 3 | Store vectors in vector database (e.g., Pinecone, FAISS, Weaviate) |
| 4 | Index for fast similarity search |
Slide 6 of 9
Slide 6 - Retrieval
- Embed user query
- Perform vector similarity search (cosine, Euclidean)
- Retrieve top-k chunks (k=5-20)
- Filter by score threshold
---
Photo by Deng Xiang on Unsplash
Slide 7 of 9
Slide 7 - Reranking
- Refine top-k retrieved chunks for relevance
- Use cross-encoder models (e.g., BGE-reranker)
- Re-score query-chunk pairs
- Select final top-m (m<k) for LLM
Slide 8 of 9
Slide 8 - LLM Processing
- Construct prompt: query + reranked context
- Feed to LLM (e.g., GPT-4, Llama)
- Generate grounded response
- Optional: faithfulness check
---
Photo by Microsoft Copilot on Unsplash
Slide 9 of 9
Slide 9 - RAG Pipeline Summary
RAG enables accurate, context-aware LLM responses by combining retrieval with generation
Key Benefits: Reduces hallucinations, leverages external knowledge, scalable
---
Photo by Kelly Sikkema on Unsplash
Discover More Presentations
Explore thousands of AI-generated presentations for inspiration
Powered by AI
Create Your Own Presentation
Generate professional presentations in seconds with Karaf's AI. Customize this presentation or start from scratch.