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.
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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.
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Prompt
Implement a complete RAG pipeline for [PROJECT NAME], a Q&A system over [DOCUMENT TYPES] using [PYTHON/NODE.JS].\\\\\\\\n\\\\\\\\n**Context:**\\\\\\\\n- Volume: [NUMBER] documents ([TYPES: PDF, DOCX, spreadsheets, emails])\\\\\\\\n- Total size: [VALUE] GB of text\\\\\\\\n- Primary language: [ENGLISH/SPANISH/BOTH]\\\\\\\\n- Generation model: [GPT-4/Claude/local Llama]\\\\\\\\n- Embeddings model: [OpenAI/Cohere/Sentence-Transformers]\\\\\\\\n- Vector DB: [PINECONE/QDRANT/PGVECTOR/CHROMADB]\\\\\\\\n\\\\\\\\n**1) Ingestion and Processing:**\\\\\\\\n- Text extraction by document type:\\\\\\\\n - PDF: [PyPDF2/pdfplumber/unstructured]\\\\\\\\n - DOCX: [python-docx]\\\\\\\\n - Spreadsheets: conversion to descriptive text\\\\\\\\n - Images/Tables: OCR or multimodal approach\\\\\\\\n- Cleaning: remove headers/footers, normalize encoding\\\\\\\\n- Metadata extraction: title, author, date, category\\\\\\\\n\\\\\\\\n**2) Chunking Strategy:**\\\\\\\\n- Method: [recursive character/semantic/sentence window]\\\\\\\\n- Chunk size: [256/512/1024] tokens — justify your choice\\\\\\\\n- Chunk overlap: [50/100/200] tokens\\\\\\\\n- Preserve context: keep whole paragraphs when possible\\\\\\\\n- Metadata per chunk: source document, page, section, date\\\\\\\\n- Parent document retrieval (small chunk for search, larger document for context)\\\\\\\\n\\\\\\\\n**3) Embeddings:**\\\\\\\\n- Model: [text-embedding-3-small/large, multilingual-e5, bge-m3]\\\\\\\\n- Dimensions: [256/768/1024/1536]\\\\\\\\n- Batch processing for large volumes\\\\\\\\n- Estimated cost for embedding the entire corpus\\\\\\\\n- Re-embedding strategy (when documents change)\\\\\\\\n\\\\\\\\n**4) Vector Database:**\\\\\\\\n- Index schema (embeddings + metadata)\\\\\\\\n- Indexing strategy (HNSW, IVF)\\\\\\\\n- Metadata filters (search only documents from [DATE], [CATEGORY])\\\\\\\\n- Hybrid search: semantic + keyword (BM25)\\\\\\\\n- Reranking: Cohere Rerank or cross-encoder\\\\\\\\n\\\\\\\\n**5) Retrieval and Generation:**\\\\\\\\n- Top K chunks to retrieve: [3/5/10]\\\\\\\\n- Prompt template with injected context:\\\\\\\\n \\\\\\\\\\\\\\\\\\\\\\\`\\\\\\\\\\\\\\\\\\\\\\\`\\\\\\\\\\\\\\\\\\\\\\\`\\\\\\\\n Based on the following company documents:\\\\\\\\n {context}\\\\\\\\n \\\\\\\\n Answer the question: {question}\\\\\\\\n \\\\\\\\n Cite the sources used.\\\\\\\\n \\\\\\\\\\\\\\\\\\\\\\\`\\\\\\\\\\\\\\\\\\\\\\\`\\\\\\\\\\\\\\\\\\\\\\\`\\\\\\\\n- Streaming response\\\\\\\\n- Citation/source attribution (reference original document)\\\\\\\\n- Fallback when confidence is low: \\\\\\\\Open directly in an AI — the text is pre-filled:
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- 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.
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