# Querying your documents Uploading a document is only the first step. The real value comes from what you can do with it. GLBNXT Workspace allows you to query your uploaded documents through natural language, asking questions, requesting specific information, and directing the AI to reason over the content in ways that would take significantly longer to do manually. This section covers how to query your documents effectively and how to get consistently accurate, useful results. *** ### How Querying Works When you upload a document and ask a question, the Workspace interface retrieves the most relevant sections of the document and passes them to the AI model as context, alongside your prompt. The model then generates a response grounded in that content. This process, known as retrieval-augmented generation, means the AI is not relying on its general training data to answer your question. It is reading your document and responding based on what is actually in it. This makes responses significantly more accurate and relevant for document-specific tasks than asking the same question without any uploaded content. *** ### Writing Effective Document Queries The quality of your results depends heavily on how you frame your prompt. Unlike a keyword search, querying documents in Workspace works best when you communicate clearly and specifically, as you would when asking a knowledgeable colleague to review a document on your behalf. A few principles that consistently produce better results: **Be specific about what you want.** Vague prompts produce vague results. Instead of asking "what does this document say", ask "what are the termination conditions described in section four of this contract" or "summarise the key financial obligations of each party." **Name the document when working with multiple files.** If you have uploaded more than one document, specify which one you are referring to in your prompt to avoid ambiguity. **Specify the output format you need.** If you want a bullet point list, a table, a paragraph summary, or a specific structure, say so. The AI will follow your formatting instructions and return content in the form that is most useful to you. **Break complex tasks into steps.** Rather than asking the AI to do everything in one prompt, work through a document progressively. Ask for a summary first, then drill into specific sections, then ask for a comparison or assessment based on what you have found. **Use follow-up questions.** Querying documents is a conversation, not a single transaction. If the first response does not cover what you need, ask a follow-up. The AI retains the context of the uploaded document and your previous exchanges throughout the thread. *** ### Common Querying Tasks The following are examples of how document querying is used in practice across different professional contexts. **Extracting specific information.** Find a particular clause in a contract, identify the methodology section of a research report, or locate the deadline dates within a tender document. Rather than reading through the entire file, ask the AI to find and return exactly what you need. **Summarising at different levels.** Request a one-paragraph executive summary of a lengthy report, or ask for a section-by-section summary that preserves more detail. You can control the level of compression and the focus of the summary through your prompt. **Identifying risks and issues.** Ask the AI to review a contract for liability clauses, flag any non-standard terms, or identify areas where the document deviates from a set of criteria you define. This is particularly useful for legal, compliance, and procurement workflows. **Comparing documents.** Upload two versions of a document and ask the AI to identify what has changed, or upload two separate agreements and ask it to highlight where the terms differ. The AI can surface differences that would be time-consuming to identify through manual review. **Answering questions grounded in source material.** Instead of relying on the AI's general knowledge to answer a domain-specific question, upload the relevant reference document and ask your question in that context. The AI will draw its answer from the uploaded content, reducing the risk of inaccurate or hallucinated information. **Drafting based on document content.** Use a document as the source material for generating new content. Ask the AI to write a briefing note based on a report, draft a response letter informed by an incoming document, or produce a set of action points from a meeting transcript. *** ### Understanding the Limits of Document Querying The embedded document querying capability within Workspace is designed for focused, session-based work with individual documents or small document sets. It is important to understand where its boundaries lie. **Context window limits apply.** Each AI model has a maximum context window, which determines how much text it can process at one time. For very long documents, only the most relevant sections are retrieved and passed to the model at any given point. This means the AI may not have visibility of the entire document simultaneously, and for very large files, some content may fall outside its active context. **Retrieval accuracy varies.** The process of identifying which sections of a document are most relevant to your query works well for most professional documents, but it is not infallible. Highly technical documents, files with complex layouts, or content where meaning depends heavily on visual structure, such as tables embedded in PDFs, may produce less accurate retrieval. **Session-based storage.** Documents uploaded in Workspace are tied to the conversation they were uploaded in. There is no persistent, organisation-wide knowledge base that indexes documents across users or sessions. Each conversation starts with a fresh context. For organisations that need to query large volumes of documents, maintain persistent knowledge bases, or require higher retrieval precision for complex content, the GLBNXT Platform provides a dedicated knowledge and data infrastructure layer built for these requirements. *** ### Verifying AI Responses Against Source Documents When querying documents for professional or high-stakes purposes, it is good practice to verify the AI's responses against the source material, particularly for tasks such as contract review, compliance checks, or regulatory analysis. The AI will generally indicate which part of the document it is drawing from in its response, and you can ask it to quote the relevant passage directly if you need to confirm the source of a specific claim. Treating AI-generated document analysis as a first-pass review rather than a final output, and applying human judgement to the results, will consistently produce the most reliable outcomes. --- # Agent Instructions: Querying This Documentation If you need additional information that is not directly available in this page, you can query the documentation dynamically by asking a question. Perform an HTTP GET request on the current page URL with the `ask` query parameter: ``` GET https://docs.glbnxt.com/workspace/knowledge-and-data/querying-your-documents.md?ask= ``` The question should be specific, self-contained, and written in natural language. The response will contain a direct answer to the question and relevant excerpts and sources from the documentation. Use this mechanism when the answer is not explicitly present in the current page, you need clarification or additional context, or you want to retrieve related documentation sections.