AI Research Assistant

The problem

Researching a technical topic often means moving between search results, papers, documentation, notes, and long documents.

A general-purpose chatbot can summarize text, but it does not automatically provide a reliable research workflow.

The goal of this project is to build an assistant that can:

• search a curated document collection
• retrieve relevant passages
• summarize evidence
• compare sources
• answer questions using retrieved information
• cite the sources used in the response

The emphasis is not on building another chatbot.

The emphasis is on building a grounded research system.

What you are building

The final application should allow a user to ask a question such as:

How does retrieval-augmented generation reduce the
need to encode changing knowledge in model parameters?

The system should then:

1. Understand the query.
2. Search the indexed knowledge base.
3. Retrieve relevant passages.
4. Rank the evidence.
5. Build a context-aware prompt.
6. Generate an answer.
7. Attach citations to the supporting sources.

A useful high-level flow is:

User Question
      ↓
Query Processing
      ↓
Semantic Search
      ↓
Retrieval
      ↓
Reranking
      ↓
Context Construction
      ↓
LLM
      ↓
Answer + Citations

Why build this project?

This project combines several skills that matter in modern LLM engineering:

• LLM APIs
• embeddings
• retrieval
• prompt design
• document processing
• backend development
• evaluation
• citations
• failure analysis

It is also a good bridge between a simple RAG chatbot and a larger production AI platform.

Project architecture

A practical architecture can look like:

                         ┌──────────────────┐
                         │   Web Interface  │
                         └────────┬─────────┘
                                  ↓
                         ┌──────────────────┐
                         │    FastAPI       │
                         └────────┬─────────┘
                                  ↓
                         ┌──────────────────┐
                         │ Research Layer   │
                         └────────┬─────────┘
                                  ↓
              ┌───────────────────┼───────────────────┐
              ↓                   ↓                   ↓
        Query Rewriter       Retriever            Metadata
                                  ↓
                             Reranker
                                  ↓
                         Retrieved Evidence
                                  ↓
                             LLM / Model
                                  ↓
                         Answer + Citations
                                  ↓
                           Evaluation / Logs

The architecture does not need to be this complex in version one.

Start with retrieval and grounded generation, then add the harder components.

Step 1 — Define the research domain

Do not begin with the entire internet.

Choose a focused domain such as:

• machine learning papers
• AI engineering documentation
• cybersecurity research
• a university subject
• a software library’s documentation

A focused collection makes evaluation easier and produces a more useful demonstration.

Step 2 — Collect documents

Gather documents from reliable sources.

Depending on your domain, documents may include:

• PDF papers
• Markdown files
• HTML documentation
• technical reports
• textbooks
• project documentation

Store metadata alongside each document:

title
author
source
publication date
URL
document type

Metadata will become important when producing citations.

Step 3 — Process and chunk documents

Large documents should be split into smaller passages.

A simplified pipeline:

Document
   ↓
Text Extraction
   ↓
Cleaning
   ↓
Chunking
   ↓
Metadata

Each chunk should retain information that identifies where it came from.

For example:

{
  "document": "attention-is-all-you-need",
  "section": "Encoder and Decoder Stacks",
  "chunk_id": "enc-dec-04",
  "text": "..."
}

This allows the application to trace retrieved evidence back to its source.

Step 4 — Generate embeddings

Convert each chunk into an embedding.

Conceptually:

Text Chunk
    ↓
Embedding Model
    ↓
Vector

Store the vectors alongside the document metadata.

You can use a vector-enabled database or another suitable vector index.

The important architectural requirement is that you can retrieve the original chunk and its metadata from the vector representation.

Step 5 — Implement semantic search

When the user asks a question:

Question
   ↓
Query Embedding
   ↓
Vector Search
   ↓
Top-K Results

Start with a simple similarity search.

Then inspect the retrieved passages manually.

Ask:

• Are they actually relevant?
• Do they answer the question?
• Are important passages missing?
• Are unrelated passages being retrieved?

This is where your project starts becoming an engineering exercise rather than a framework demo.

Step 6 — Add reranking

Semantic similarity alone may not always produce the best final ranking.

Add a reranking stage:

Top-K Retrieved Chunks
        ↓
Reranker
        ↓
Best Evidence

The reranker can use a stronger relevance model to score query-document pairs.

This gives you a useful experiment:

Vector Search
       vs.
Vector Search + Reranking

Measure whether reranking actually improves your evaluation results.

Step 7 — Build the LLM prompt

Construct a prompt that separates:

System Instructions
User Question
Retrieved Evidence
Output Requirements

For example, the application should make it clear that:

• retrieved passages are evidence
• unsupported claims should not be invented
• citations should point to the supplied sources
• uncertainty should be stated when evidence is insufficient

The exact prompt will depend on your model.

Do not blindly copy a generic RAG prompt.

Create one that matches your evaluation criteria.

Step 8 — Generate grounded answers

Pass the selected evidence to the model.

The model should generate:

Answer
+
Source References

A good response might look conceptually like:

RAG reduces dependence on model parameters for changing knowledge
by retrieving external information at inference time. [1][2]

Sources:
[1] Paper A
[2] Documentation B

The citation system should be generated from retrieved metadata rather than allowing the model to invent URLs.

Step 9 — Build a source panel

Show users the evidence used to generate the answer.

For example:

Sources
──────────────────────────
[1] Attention Is All You Need
    arXiv · 2017

[2] RAG for Knowledge-Intensive NLP
    arXiv · 2020

Allow the user to open the original source.

This improves transparency and makes the application more useful for serious research.

Step 10 — Add conversation context

Research questions often evolve.

A user might ask:

What is RAG?
   ↓
How does it compare with fine-tuning?
   ↓
When should I use each?

Store limited conversation context so follow-up questions can be interpreted correctly.

Do not retain everything forever by default.

Keep context relevant and manage its size.

Step 11 — Evaluate retrieval

Create an evaluation dataset.

Example:

Question:
What problem does RAG solve?

Expected Evidence:
Document A, section 2

Then measure retrieval quality.

Useful metrics include:

• Recall@K
• Precision@K
• MRR
• nDCG

You don’t need every metric.

Start with one or two that answer your main question.

Step 12 — Evaluate generated answers

Retrieval quality is only half of the system.

You also need to evaluate the final response.

Create questions where you know what a good answer should contain.

Measure:

• correctness
• relevance
• groundedness
• citation correctness
• completeness

For high-quality evaluation, combine automated metrics with human review.

Step 13 — Test failure cases

Deliberately test situations where the system should struggle.

Examples:

Unsupported question

Ask something that does not exist in the document collection.

Expected behavior:

"I don't have enough evidence in the indexed sources
to answer this confidently."

Ambiguous question

The system should ask for clarification when needed.

Conflicting sources

The application should identify the disagreement instead of silently choosing one source.

Poor retrieval

The system should avoid presenting weak evidence as fact.

These cases are more valuable than a perfect demo question.

Step 14 — Build the API

Expose the research assistant through FastAPI.

A request could look like:

{
  "question": "What are the main limitations of RAG?"
}

A response could look like:

{
  "answer": "...",
  "sources": [
    {
      "title": "Source A",
      "url": "https://example.com/source-a"
    }
  ]
}

Add:

• request validation
• timeouts
• retries
• structured errors
• logging

The API should fail cleanly when the model or retrieval service is unavailable.

Step 15 — Add observability

Track:

request_id
query
retrieved_documents
model
latency
token_usage
errors
evaluation_score

Do not log sensitive document contents or user data unnecessarily.

The goal is to make debugging possible without creating a new privacy problem.

What you will learn

By completing this project, you should understand:

• LLM application architecture
• embeddings
• semantic search
• vector retrieval
• reranking
• prompt construction
• RAG
• citation systems
• API design
• evaluation
• error analysis
• observability

Tech stack

A practical implementation can use:

Python
FastAPI
PostgreSQL + vector search
Embedding Model
LLM API / Open Model
Docker
Git + GitHub

You can replace individual components later.

The concepts matter more than the specific vendor or framework.

Version roadmap

Version 1 — Basic Research Assistant

Documents
 ↓
Embeddings
 ↓
Vector Search
 ↓
LLM
 ↓
Answer

Goal: understand the fundamental RAG loop.

Version 2 — Grounded Research Assistant

Add:

• citations
• metadata
• source panel
• evaluation dataset

Version 3 — Better Retrieval

Add:

• reranking
• hybrid search
• query rewriting
• metadata filters

Version 4 — Production System

Add:

• authentication
• document permissions
• background ingestion
• caching
• logging
• monitoring
• Docker
• CI/CD

Version 5 — Research Workspace

Add:

• saved conversations
• collections
• source comparison
• notes
• export
• collaborative workflows

Do not build version five before version one works.

Possible improvements

Once the core system is stable, experiment with:

• hybrid keyword + semantic search
• query expansion
• reranking
• source credibility scoring
• citation validation
• document freshness tracking
• multilingual retrieval
• multimodal document support
• user feedback loops
• automated evaluation

Each improvement should be measured.

Common mistakes

Building a generic chatbot

The value of this project is the research workflow.

Using retrieval without measuring it

You need to know whether the right evidence is being retrieved.

Letting the model invent citations

Citations should come from your application’s source metadata.

Adding too much context

More retrieved text does not automatically mean a better answer.

Ignoring document quality

Bad source material produces bad answers.

Fine-tuning too early

First establish whether retrieval, prompting, or architecture solves the actual problem.

Skipping evaluation

A good demo is not evidence that the system works reliably.

What makes this project portfolio-worthy?

A basic RAG chatbot is now a common project.

Make your version technically stronger.

Show:

Baseline Retrieval
      ↓
Reranking
      ↓
Evaluation
      ↓
Citation Validation
      ↓
Production API
      ↓
Monitoring

Then document the results.

For example:

Adding reranking improved Recall@5 from 0.72 to 0.86 on the evaluation dataset while adding 90 ms of average latency.

That is a meaningful engineering result.

GitHub repository structure

A practical structure could be:

ai-research-assistant/
│
├── app/
│   ├── api/
│   ├── retrieval/
│   ├── generation/
│   ├── evaluation/
│   └── services/
│
├── data/
├── tests/
├── scripts/
│
├── Dockerfile
├── README.md
├── requirements.txt
└── .gitignore

Keep the README focused on:

• problem
• architecture
• setup
• evaluation
• results
• limitations
• demo

Final challenge

After completing the basic application, remove the tutorial.

Then rebuild the system independently.

Try to answer:

1. Can I improve retrieval?
2. Can I prove that it improved?
3. Can I trace every answer to its evidence?
4. Can I detect unsupported questions?
5. Can I deploy it?
6. Can I explain every architectural choice?

When you can answer those questions confidently, you have moved beyond building a demo.

You are doing LLM engineering.

What you’ll learn

This project prepares you for more advanced LLM systems:

Semantic Search
      ↓
RAG
      ↓
Research Assistant
      ↓
Tool-Using Agents
      ↓
Multimodal Systems
      ↓
Production AI Platforms

The main lesson is simple:

An LLM application becomes useful when the system around the model is engineered well.