Document Ingestion
Prepare supported research documents for indexing and retrieval, with clear feedback when files cannot be processed.
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Generative AI · Research Productivity
Research Copilot
An AI-assisted research workflow concept for discovering, exploring, and summarizing knowledge from academic documents, with retrieval-augmented generation (RAG) as a core technical approach.
Generative AI
RAG
Azure OpenAI
Azure AI Search
Python
Research Copilot
Research workspace concept
Ask your research library
What methods are used to evaluate edge intelligence systems?
Add research documents
Example answer format
Summarize relevant findings and connect each key claim to source passages so the researcher can verify the evidence.
Source 1Source 2
Retrieval · Context · Response
Illustrative UI preview — not a screenshot of a released application.
Project scope and evidence
This page describes the intended workflow and technology direction. Replace illustrative UI and general feature descriptions with verified screenshots, implemented capabilities, evaluation results, and deployment details as they become available. Do not present planned functionality as production-ready.
Project Overview
Make research documents easier to explore
Research Copilot is an AI-assisted research application concept intended to help users work with academic knowledge. A retrieval-augmented generation workflow can retrieve relevant passages from a selected document collection and provide them as context for an AI-generated response.
Potential use cases include summarizing papers, asking questions across a research library, comparing methods, extracting key findings, and organizing material for a literature review. Outputs should support research judgment rather than replace reading, source verification, or scholarly attribution.
Potential Capabilities
Support the research journey
These capabilities describe the product direction and should be marked as implemented only after verification.
Question Answering
Ask questions grounded in a selected research collection and inspect the supporting source passages.
Paper Summaries
Generate structured summaries of research goals, methods, findings, limitations, and future work.
Literature Review Support
Help organize themes, compare approaches, and identify questions for further investigation.
Source Traceability
Present source references or retrieved excerpts so users can verify claims against original documents.
Retrieval-Augmented Generation
Combine document retrieval with language-model generation to ground responses in relevant context.
Proposed Architecture
A document-grounded RAG workflow
This conceptual flow illustrates one possible architecture. Adapt it to the components and configuration used by the actual implementation.
01
Ingest
Upload supported research files and extract text with validation and error handling.
02
Prepare & Index
Split text into useful passages, attach metadata, and index searchable content.
03
Retrieve Context
Use a question to find relevant passages from the selected collection.
04
Generate Answer
Send the question and retrieved context to the model and show source references.
Research documents → Text extraction → Chunking & metadata → Search / retrieval → LLM response + source references
Technology Direction
Cloud AI components with a Python application layer
The technologies below reflect the intended stack described for this project. Confirm which services and libraries are used in the deployed version before publishing final implementation claims.
Python
Application logic and API integration
Azure OpenAI
Language-model responses
Azure AI Search
Search and retrieval over indexed content
RAG
Ground generation in retrieved document context
Evaluation & Quality
Measure retrieval and answer quality
A research assistant should be evaluated on whether it retrieves the right evidence, answers faithfully, and makes verification practical. The checklist below is a proposed evaluation plan, not a report of measured results.
- Verify extracted text against source documents
- Test retrieval relevance for a curated question set
- Check that answers are supported by retrieved passages
- Test unknown-answer and insufficient-context behavior
- Validate source references and page or section metadata
- Test malformed, scanned, and unsupported files
- Review latency, token use, and cost per query
- Check document access controls and sensitive-data handling
Responsible Research
AI assistance should not replace scholarly judgment
Generated summaries and answers can omit nuance or misinterpret a source. Researchers should check original papers, verify citations, and make their own judgments before relying on outputs in academic work.
- Verify claims in original source documents.
- Distinguish source text from AI-generated synthesis.
- Respect copyright, licensing, and institutional policies.
- Protect confidential, unpublished, or sensitive research files.
- Do not treat generated references as valid until checked.
Potential Roadmap
From document Q&A to research workflow support
Prioritize the roadmap based on user feedback, technical evaluation, and available resources.
Phase 1 · Document Q&A
Stabilize ingestion, retrieval, answers, and source references for a small document collection.
Phase 2 · Research Workspace
Explore document organization, saved queries, and notes linked to source passages.
Phase 3 · Literature Synthesis
Compare methods, datasets, findings, and limitations across selected papers.
Phase 4 · Evaluation
Build repeatable tests for retrieval quality, faithfulness, latency, and cost.
Phase 5 · Collaboration
Evaluate secure workspaces and sharing with clear document-level permissions.
Phase 6 · Research Integrations
Consider export formats and integrations with researcher workflows where feasible.
Explore More Work
Interested in research-focused AI?
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