This content originally appeared on DEV Community and was authored by Noel Alex
Hey everyone, I'm Noel Alex from VIT Vellore! đź‘‹
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:
Streamlitfor 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.llmwarefor 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 likejina-embeddings-v2. It basically builds the brain of my operation.Groqfor 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 powerfulLlama-3.3-70Bmodel, 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" )llmwaretakes care of creating a library, parsing the docs, and embedding them into a localChromaDBvector 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
This content originally appeared on DEV Community and was authored by Noel Alex
Noel Alex | Sciencx (2025-10-03T16:49:28+00:00) Tired of AI Hallucinations? I Built a RAG App to Keep My Research Grounded.. Retrieved from https://www.scien.cx/2025/10/03/tired-of-ai-hallucinations-i-built-a-rag-app-to-keep-my-research-grounded/
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