RAG Reliability Lab

A reliability workbench for RAG systems: inspect chunks, compare retrieval strategies, validate answer sources, and turn weak questions into repeatable checks.

This project is not a generic knowledge-base chatbot. It is a small engineering console for understanding whether a RAG system is finding the right evidence, using it correctly, and improving after each retrieval change.

Why this project exists

Many RAG demos produce fluent answers without showing enough evidence. In real work, that is not enough. A useful internal knowledge assistant must make the search path visible.

A reliable RAG system should answer:

1. What content was indexed?
2. How was the content split into chunks?
3. Which retrieval method found the relevant evidence?
4. Are answer sources visible and useful?
5. Did a new setting improve or weaken quality?

Product shape

The product should feel like a compact RAG quality console, not a chat app first.

Core screens:

Screen	What it shows
Documents	Imported files, source type, parsed size, index status
Chunk Inspector	Chunk text, source reference, section path, token count
Search Comparison	BM25, vector, hybrid, and reranked results side by side
Answer With Sources	Final answer next to the chunks that support it
Metrics	Recall@5, MRR, citation coverage, latency, and cost estimate
Saved Cases	Questions that should be checked again after changes

MVP target

Build a local RAG lab that can ingest documents, compare BM25/vector/hybrid retrieval, generate cited answers, and report basic quality metrics.

documents -> chunks -> search comparison -> answer sources -> metrics -> saved cases

The first useful demo should let a reviewer understand why an answer was produced in under three minutes.

Prioritized roadmap

Priority	Workstream	Outcome
P0 / MVP	Document ingestion and chunk preview	Retrieval quality can be debugged before embeddings
P0 / MVP	Vector and BM25 retrieval	Two strong baselines are visible
P0 / MVP	Hybrid retrieval and rerank	The project demonstrates real retrieval tuning
P0 / MVP	Showcase path	One demo path from documents to answer sources and metrics
P1	Evidence panel and citation validation	Answers become inspectable and source-linked
P1	Eval dataset and metrics	Quality can be measured after each retrieval change
P1	Saved case board	Weak questions become reusable checks
P2	Interview notes and demo script	The project is easy to explain to hiring teams

Repository documents

• Business Context
• Architecture
• Data Model
• Roadmap
• Showcase Plan
• Design Gallery

Demo narrative

1. Import a small document pack.
2. Inspect chunks before retrieval.
3. Ask a realistic question.
4. Compare search methods.
5. Show the answer and sources.
6. Save the question as a repeatable case.
7. Show a small metrics table.

What this project demonstrates

• Practical RAG engineering beyond embedding calls.
• Understanding of retrieval failure modes.
• Product judgment around trust and evidence.
• Clear presentation of quality, latency, and cost trade-offs.
• English technical documentation suitable for remote interviews.

Status

Planning and scaffolding. Issues are used as the implementation roadmap. The next build target is the P0 MVP showcase path.