Overview
Ingeniometrix Research Assistant is a concept for disciplined technical research support using retrieval, summarization, and source-aware note generation.
Role / Contribution
- Designed the workflow boundaries for source collection, chunking, retrieval, structured note generation, and reviewer approval.
System Architecture
Step 01
Sources
Step 02
Chunking
Step 03
Embeddings
Step 04
Retriever
Step 05
LLM Notes
Step 06
Review
Primary Flow
Sources→Chunking→Embeddings→Retriever→LLM Notes→Review
Data Flow
- Technical sources are loaded with metadata and citation context.
- Text is chunked and indexed for retrieval.
- Relevant passages are retrieved for a research question.
- The model generates structured notes with supporting references.
- A reviewer confirms accuracy before notes are reused.
Technical Components
Document ingestion
Embedding index concept
Retrieval pipeline
Structured notes schema
Citation metadata
JSON Output Example
{
"query": "Summarize retrofit considerations for legacy data workflows.",
"answer_type": "research_note",
"findings": [
{
"claim": "Legacy workflow modernization should preserve audit checkpoints.",
"support_level": "medium",
"source_ids": [
"SRC-001",
"SRC-004"
]
}
],
"review_status": "pending"
}Engineering Notes
- Retrieval should prioritize source quality and recency, not only semantic similarity.
- Generated summaries should expose source coverage and uncertainty instead of masking gaps.
Key Takeaways
- Demonstrates RAG workflow planning.
- Shows attention to source traceability and reviewability.
- Frames AI as research support rather than autonomous authority.