RAG Pipeline (Retrieval-Augmented Generation) with Embeddings and Vector Database
Complete RAG implementation covering chunking, embeddings, semantic search, and augmented generation.
Build a document-based Q&A system that reduces hallucinations and delivers answers grounded in your actual data.
At a glance
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Prompt objective
Build a document-based Q&A system that reduces hallucinations and delivers answers grounded in your actual data.
Real use case
A Rio de Janeiro-based consultancy called DataInsight has accumulated 2,500 PDF reports, 800 spreadsheets, and 15,000 technical emails over 8 years. Their consultants spend 2-3 hours daily searching for information from past projects. They need a chatbot that answers questions using this knowledge base.
Customize these fields first
Replace the placeholders with your own context before you run the prompt. That usually improves the first output more than adding more instructions later.
Prompt
Implement a complete RAG pipeline for [PROJECT NAME], a Q&A system over [DOCUMENT TYPES] using [PYTHON/NODE.JS].
**Context:**
- Volume: [NUMBER] documents ([TYPES: PDF, DOCX, spreadsheets, emails])
- Total size: [VALUE] GB of text
- Primary language: [ENGLISH/SPANISH/BOTH]
- Generation model: [GPT-4/Claude/local Llama]
- Embeddings model: [OpenAI/Cohere/Sentence-Transformers]
- Vector DB: [PINECONE/QDRANT/PGVECTOR/CHROMADB]
**1) Ingestion and Processing:**
- Text extraction by document type:
- PDF: [PyPDF2/pdfplumber/unstructured]
- DOCX: [python-docx]
- Spreadsheets: conversion to descriptive text
- Images/Tables: OCR or multimodal approach
- Cleaning: remove headers/footers, normalize encoding
- Metadata extraction: title, author, date, category
**2) Chunking Strategy:**
- Method: [recursive character/semantic/sentence window]
- Chunk size: [256/512/1024] tokens — justify your choice
- Chunk overlap: [50/100/200] tokens
- Preserve context: keep whole paragraphs when possible
- Metadata per chunk: source document, page, section, date
- Parent document retrieval (small chunk for search, larger document for context)
**3) Embeddings:**
- Model: [text-embedding-3-small/large, multilingual-e5, bge-m3]
- Dimensions: [256/768/1024/1536]
- Batch processing for large volumes
- Estimated cost for embedding the entire corpus
- Re-embedding strategy (when documents change)
**4) Vector Database:**
- Index schema (embeddings + metadata)
- Indexing strategy (HNSW, IVF)
- Metadata filters (search only documents from [DATE], [CATEGORY])
- Hybrid search: semantic + keyword (BM25)
- Reranking: Cohere Rerank or cross-encoder
**5) Retrieval and Generation:**
- Top K chunks to retrieve: [3/5/10]
- Prompt template with injected context:
```
Based on the following company documents:
{context}
Answer the question: {question}
Cite the sources used.
```
- Streaming response
- Citation/source attribution (reference original document)
- Fallback when confidence is low: Open directly in an AI — the text is pre-filled:
How to use this prompt
- 1Replace the key placeholders first: PROJECT NAME, DOCUMENT TYPES, PYTHON/NODE.JS, NUMBER.
- 2Replace any bracketed placeholders like [this] with your own context.
- 3Add extra background information when you want more tailored results.
- 4Combine multiple prompts in one conversation when you need a richer output.
- 5Save your best-performing prompts so they are easy to reuse later.
Next best step
Open the guide first, then branch only if you still need more.
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