Skill Guide

Retrieval-Augmented Generation (RAG) pipeline design for long documents

RAG pipeline design for long documents is the architectural engineering of retrieval, chunking, and generation systems that maintain context and accuracy across documents exceeding standard LLM context limits.

Organizations value this skill because it enables accurate, grounded AI responses from proprietary knowledge bases (contracts, manuals, research papers) while mitigating hallucination risks. Directly impacts operational efficiency, compliance accuracy, and knowledge worker productivity.

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How to Learn Retrieval-Augmented Generation (RAG) pipeline design for long documents

1. Core RAG architecture (retriever-generator loop). 2. Document chunking strategies (fixed-size, semantic, recursive). 3. Embedding model selection and vector database basics (Pinecone, Weaviate, Chroma).

1. Implement hierarchical retrieval (parent-child chunk relationships). 2. Apply re-ranking models (Cohere, cross-encoders) to improve precision. 3. Handle multi-document queries and deduplication. Avoid naive fixed-size chunking without overlap; test retrieval recall rigorously.

1. Design hybrid search (keyword + vector + knowledge graph). 2. Implement query decomposition for complex questions. 3. Architect feedback loops with human-in-the-loop validation. Focus on latency-cost-accuracy tradeoffs and production monitoring.

Practice Projects

Beginner

Project

Legal Contract Q&A System

Scenario

Build a RAG pipeline to answer questions from a 50-page employment contract PDF.

How to Execute

1. Extract text with PyPDF2 or Tesseract (if scanned). 2. Chunk using LangChain's RecursiveCharacterTextSplitter (1000 tokens, 200 overlap). 3. Embed with OpenAI Ada-002, store in ChromaDB. 4. Build retrieval chain with similarity search + GPT-3.5 summarization.

Intermediate

Project

Technical Documentation Multi-Query System

Scenario

Create a system that handles technical questions requiring information from multiple sections of a 200-page software manual.

How to Execute

1. Implement semantic chunking with sentence-transformers. 2. Build parent-child document hierarchy (small chunks for retrieval, large chunks for context). 3. Add HyDE (Hypothetical Document Embeddings) for better query matching. 4. Implement Cohere re-ranking on top-10 results.

Advanced

Project

Regulatory Compliance Audit Assistant

Scenario

Design a system for auditors to query across 500+ pages of financial regulations, with source attribution and confidence scoring.

How to Execute

1. Build hybrid search combining BM25, vector, and knowledge graph relationships. 2. Implement query decomposition (break complex questions into sub-queries). 3. Add citation tracking with precise page/paragraph references. 4. Create feedback loop where auditors flag inaccuracies for model retraining. 5. Implement latency optimization with caching and pre-computation.

Tools & Frameworks

Software & Platforms

LangChain/LlamaIndexPinecone/Weaviate/MilvusCohere RerankHaystack

Use LangChain for pipeline orchestration, vector DBs for storage, Cohere for precision improvement, Haystack for production-ready search systems.

Embedding Models

OpenAI Ada-002Sentence-Transformers (all-MiniLM-L6-v2)BGE-M3

Ada-002 for general quality, sentence-transformers for cost efficiency, BGE-M3 for multilingual support.

Evaluation Frameworks

RAGASDeepEvalTruLens

Use RAGAS for comprehensive metrics (faithfulness, relevance), TruLens for real-time monitoring in production.

Interview Questions

Answer Strategy

Use hierarchical retrieval: small chunks for precise matching, larger parent chunks for context. Implement query decomposition to break complex questions into sub-queries. Add re-ranking with cross-encoders and maintain citation tracking to source paragraphs. Example: 'I'd implement a three-stage retrieval: first pass with semantic search on 256-token chunks, then expand context to 2048-token parent chunks, finally apply Cohere re-ranking for precision.'

Answer Strategy

Testing knowledge of user experience optimization. Response: 'I'd analyze retrieval logs to see if relevant chunks are being selected but poorly ordered. Solution: implement passage reordering models, use hierarchical summarization (chunk → section → document), and add a final synthesis step in the generator prompt that explicitly requires coherent narrative flow.'