AI Consulting & AI Services

Hook Employees often ask the same questions about HR policies, contracts, pricing, product documents, or technical manuals. Yet the answers remain scattered across SharePoint, PDFs, cloud drives, databases, and internal wikis, making simple information difficult to find quickly. Introduction A Retrieval-Augmented Generation (RAG) system connects an LLM with a company’s internal knowledge. It retrieves relevant company information and uses it as context to generate grounded answers. Building one requires more than connecting documents to an AI model. Companies also need clean data, effective retrieval, access controls, testing, monitoring, and regular updates. Key Takeaways Here is a quick look at the main things you will learn in this guide: How to identify useful internal knowledge for RAG. How to clean, structure, chunk, and index company documents. How vector, keyword, hybrid search, metadata, and reranking work. How to protect restricted company information. How to test, monitor, and update the system. Table of Contents Define What Your Company RAG System Must Answer Audit and Prepare Your Internal Knowledge Choose the Right Data Sources and Retrieval Architecture Clean and Structure Company Documents Before Indexing Chunk Internal Documents for Better Retrieval Create Embeddings and Build the Knowledge Index Combine Keyword, Vector and Hybrid Search Connect Retrieved Knowledge to the LLM Protect Internal Data With Access Controls Test RAG Accuracy Before Going Live Monitor Answers, Costs and Knowledge Updates Move From Prototype to a Production RAG System Define What Your Company RAG System Must Answer Start with the questions employees need answered most often. These may involve HR policies, technical documents, contracts, product information, pricing, or compliance records. Define the users and knowledge scope at the same time. This helps determine the required sources, permissions, retrieval rules, and testing criteria. Audit and Prepare Your Internal Knowledge List every source containing relevant company information before building the retrieval layer. Include document repositories, databases, wikis, cloud storage, and business systems. Review each source for accuracy and relevance. Remove outdated files, duplicates, conflicting versions, and content without clear ownership. Map Data Sources List SharePoint, cloud drives, wikis, databases, CRM systems, and document repositories. Remove Outdated Content Archive old policies, procedures, manuals, and superseded versions. Classify Sensitive Data Separate internal, confidential, financial, employee, customer, and restricted information. Assign Content Owners Identify who reviews and updates each important knowledge source. Choose the Right Data Sources and Retrieval Architecture A typical RAG flow moves from data ingestion to processing, chunking, embeddings, indexing, retrieval, and answer generation. Choose the architecture based on data volume, document types, update frequency, security needs, and query volume. Clean and Structure Company Documents Before Indexing Raw files often contain repeated headers, broken tables, duplicate text, and irrelevant formatting. Clean these elements before indexing. Preserve titles, headings, dates, versions, authors, departments, and source locations because they provide useful retrieval context. Remove Repeated Text: Delete unnecessary headers and footers. Preserve Structure: Keep headings and section relationships. Process Tables: Extract table information accurately. Keep Document Details: Retain titles, dates, and versions. Record Ownership: Store authors and responsible teams. Save Source Locations: Keep links or identifiers for original documents. Chunk Internal Documents for Better Retrieval Chunking divides large documents into smaller searchable sections. Each chunk should contain enough context while staying focused. Use document structure to guide chunking. Policies, contracts, manuals, FAQs, and reports may require different chunking methods. Keep Related Information Together Keep connected requirements, definitions, exceptions, and explanations in the same chunk. Follow Document Structure Use headings, paragraphs, tables, and logical sections as boundaries. Add Chunk Metadata Store the document name, department, date, version, category, and access information. Test Chunk Size Compare different chunk sizes and overlap settings before selecting the final approach. Create Embeddings and Build the Knowledge Index Embeddings convert text into numerical representations that help the system find semantically related content. Store embeddings with metadata and source references so retrieved information can connect back to its original document. Choose an Embedding Model: Match the model to your content and language needs. Generate Embeddings: Create vectors for approved document chunks. Store Metadata: Attach relevant attributes to each chunk. Keep Source IDs: Preserve document and section references. Track Versions: Record the current document version. Re-index Changes: Update the index when approved content changes. Combine Keyword, Vector and Hybrid Search Vector search finds content based on meaning, while keyword search handles exact terms. Both can be useful for internal company information. Hybrid search combines these approaches, while reranking can prioritize the most relevant results before they reach the LLM. Use Vector Search Find semantically similar information despite different wording. Use Keyword Search Find exact names, codes, identifiers, and technical terms. Apply Metadata Filters Filter results by department, date, document type, region, or version. Rerank Results Reorder retrieved passages based on their relevance to the question. Connect Retrieved Knowledge to the LLM The system sends relevant retrieved passages to the LLM as context. The model then uses this information to generate the response. Set clear instructions for using sources and handling missing information. This helps reduce unsupported answers. Pass Relevant Context: Send only useful retrieved content. Use Company Sources: Instruct the model to rely on supplied evidence. Avoid Unsupported Claims: Tell it not to invent missing information. Handle Conflicts: Define how conflicting sources should be treated. Include Citations: Provide document or source references. Set Response Rules: Use a consistent answer format. Protect Internal Data With Access Controls Internal RAG systems may contain confidential employee, financial, customer, contract, and business information. Retrieval must follow existing data permissions. Check authorization before restricted content reaches the LLM. User identity, document permissions, and retrieval filters should work together. Connect User Identity Use the company’s identity system to identify the requesting user. Store Access Rules Associate documents and chunks with approved users, groups, or roles. Filter Retrieved Content Remove unauthorized content before sending results to the LLM. Maintain Access Logs Record relevant access and retrieval events for security review. Test RAG Accuracy Before Going Live A fluent answer does not prove that retrieval worked correctly. Test the retrieved content and generated answer separately. Build a […]