Building Document Question-Answering Systems with Rust

A practical two-hour session on the principles behind document question-answering systems, with Rust as the implementation context.

The session focuses on how applications can answer questions from a known set of documents instead of relying only on what a language model already knows. We will cover the core workflow: loading documents, preparing them for search, finding relevant passages, passing useful context to a model, and producing answers that stay connected to the source material.

The goal is to teach the engineering ideas behind RAG systems without turning the session into a product demo or an abstract AI lecture. The emphasis is on system boundaries, retrieval quality, answer grounding, error handling, and the practical decisions developers need to make when building document-aware applications.

Audience: entry-level and intermediate developers who want a practical session on document search, retrieval, grounded generation, and Rust application design.

Outcomes:

• Understand how document-based question answering systems work
• Explain the purpose of chunking, embeddings, retrieval, and context assembly
• Design a simple retrieve-first, answer-second workflow
• Use Rust to organize the main parts of a small RAG application
• Recognize common causes of poor or unsupported answers
• Apply basic techniques for improving retrieval and answer quality
• Think about how to evaluate whether the system is actually useful

Format: two hours with a short framing walkthrough, a concrete example, discussion of tradeoffs, and a closing checklist for practice.