Tired of AI Hallucinations? I Built a RAG App to Keep My Research Grounded.

#rag #ai #showdev #llm

Hey everyone, I'm Noel Alex from VIT Vellore! 👋

Let's be real: we're all leaning on AI pretty heavily these days. Whether it's for debugging a stubborn piece of code or just exploring a new topic, LLMs have become our go-to. But there's a huge problem, especially when you're doing serious research.

You ask a detailed question, and the AI gives you a beautifully written, confident-sounding answer that is... completely made up. It hallucinates. It invents facts, cites non-existent papers, and can send you down a rabbit hole of misinformation. For a developer or a student doing research, that's a nightmare.

I ran into this exact wall while working on a research project. I needed answers I could trust, backed by actual, verifiable sources. I didn't want to "blindly trust AI"; I wanted to use AI to augment my own intelligence, not replace my judgment.

That’s when I decided to build my own solution: a Scientific Research Agent that uses Retrieval-Augmented Generation (RAG) to give me answers grounded in reality.

The Mission: AI Answers You Can Actually Trust

The core idea behind RAG is simple but powerful: instead of letting an LLM pull answers from its vast, opaque training data, you give it a specific set of documents to use as its only source of truth.

The workflow looks like this:

You provide the knowledge: Upload a bunch of trusted research papers.
You ask a question: "What are the latest findings on quantum entanglement?"
The system retrieves: It intelligently searches only through your documents to find the most relevant paragraphs.
The AI synthesizes: It takes those relevant snippets, and your question, and crafts an answer based exclusively on that context.

No more hallucinations. No more made-up facts. Just pure, verifiable information synthesized into a coherent answer.

The Tech Stack: Building the "Grounding Engine"

I wanted this tool to be fast, efficient, and easy to use. Here’s the stack I chose to bring it to life:

Streamlit for the UI: I love Streamlit. It lets you build interactive web apps with just Python. No messy HTML or JavaScript needed. It was perfect for creating a simple interface for uploading files and asking questions.
llmware for the RAG Pipeline: This library is a beast. It handled the entire backend RAG workflow seamlessly. It takes the uploaded PDFs, parses them, breaks them into smart chunks (way better than just splitting by a fixed number of characters), and then creates vector embeddings using a top-tier model like jina-embeddings-v2. It basically builds the brain of my operation.
Groq for Blazing-Fast Inference: This was the game-changer. RAG involves sending a lot of context to the LLM, which can be slow and expensive. Groq’s LPU™ Inference Engine is absurdly fast. I used the powerful Llama-3.3-70B model, and it generates answers almost instantly. This speed makes the app feel responsive and genuinely useful, not a slow, clunky research tool.

Let's See the Code in Action

The logic is surprisingly straightforward. Here's a high-level look at the Python script (main.py):

File Upload & Processing (Sidebar):
The Streamlit sidebar has a file uploader. When I hit "Process & Embed Documents," this function kicks in:

# Simplified from the app
def process_and_embed_files(library_name, folder_path):
    library = Library().create_new_library(library_name)
    library.add_files(input_folder_path=folder_path)
    library.install_new_embedding(
        embedding_model_name=EMBEDDING_MODEL,
        vector_db="chromadb"
    )

llmware takes care of creating a library, parsing the docs, and embedding them into a local ChromaDB vector store. Easy peasy.

Asking a Question:
When a user types a query and hits "Get Answer," two things happen.

First, we perform a semantic search to find relevant context:

# Find the most relevant text chunks from the library
query_results = Query(library).semantic_query(user_query, result_count=7)

Second, we assemble a prompt with that context and send it to Groq:

# Build the prompt with clear instructions
prompt_template = """Based *only* on the provided context, answer the query.
If the context does not contain the answer, say so.

Context:
{context}

Query:
{query}
"""
context = "\n---\n".join([result['text'] for result in query_results])
final_prompt = prompt_template.format(context=context, query=user_query)

# Get the lightning-fast answer from Groq
answer = ask_groq(final_prompt, model=LLM_MODEL)
st.markdown(answer)

The key here is the prompt: "Based only on the provided context...". This is the instruction that constrains the LLM and prevents it from hallucinating.

The Final Result: An AI I Can Finally Trust for Research

What I ended up with is a personal research assistant that I can fully trust. I feed it the papers, and it gives me back synthesized knowledge from those papers alone. I can see the exact context it used, so I can always verify the source.

This project was a fantastic learning experience. It showed me that the real power of AI isn't just in its raw creative ability, but in our ability as developers to channel that power in a controlled, reliable, and useful way.

So next time you're frustrated with a chatbot giving you nonsense, remember: you have the power to ground it in reality. Give RAG a try!

You can check out the full code on my GitHub. Let me know what you think