RAG Application Template - Retrieval Augmented Generation Setup
Download our RAG application template for knowledge base chatbots and Q&A systems. Includes chunking, embeddings, vector database, and prompt design.
RAG (Retrieval Augmented Generation) combines a vector database with an LLM to build question-answering systems based on your own data. This template provides a structured approach for document processing, chunking strategies, embedding pipelines, retrieval configuration, and prompt design. Ideal for internal tools, support chatbots, and enterprise search with AI.
Variations
Document-RAG Template
Template for PDF, Word, and web content. Includes parsing, layout-aware chunking, and metadata filtering.
Best for: Suitable for knowledge bases, manuals, and internal documentation that need to be searchable via natural language.
Code-RAG Template
Template for codebases: repository indexing, code chunking at function/module level, and developer-focused retrieval.
Best for: Perfect for AI-assisted coding tools, code search, and onboarding new developers.
Multi-Source RAG Template
Template for combined sources: documents, databases, APIs. Includes source prioritisation and blended retrieval.
Best for: Ideal for enterprise applications that need to search multiple data sources in one interface.
How to use
Step 1: Download the template and identify your content sources (documents, wiki, database). Step 2: Choose a chunking strategy — overlap, semantic boundaries, or fixed size — and test on your content. Step 3: Select an embedding model and vector database (Pinecone, Weaviate, pgvector). Step 4: Build the indexing pipeline: parse → chunk → embed → store. Ensure incremental updates. Step 5: Design retrieval: number of chunks, similarity threshold, and optionally hybrid search. Step 6: Build the prompt: context injection, formatting, and instructions for "no answer found". Step 7: Evaluate with representative questions and optimise chunking, retrieval, and prompts based on results.
Frequently asked questions
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