Fast Prototyping with Streamlit & Gen AI Tools

Streamlit, an open-source Python library, makes this possible

Prototyping is the bridge between an idea and a working product. In the world of generative AI, where tools and expectations evolve quickly, the ability to move from concept to demo in hours—not weeks—is a real advantage. Streamlit, an open-source Python library, makes this possible. It allows you to turn a few lines of code into interactive, shareable web apps without needing front-end expertise.

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This post draws from the Fast Prototyping of GenAI Apps with Streamlit course by DeepLearning.AI and Snowflake . This course is taught by Chanin Nantasenamat , Sr Developer Advocate also known as the Data Professor.

Why Fast Prototyping Matters

Ideas often lose momentum when they stay in documents or slide decks. In AI, where experimentation is key, the faster you can test and share, the better. Rapid prototyping helps you:

• Validate assumptions: Instead of debating whether an idea works, you can show it.

• Gather feedback early: A working demo sparks more useful conversations than abstract descriptions.

• Iterate quickly: You can refine based on real interactions, not speculation.

• Influence decisions: Stakeholders respond to tangible prototypes more than theoretical plans.

Generative AI adds another layer: with large language models (LLMs), you can build functional prototypes with minimal code. Streamlit provides the interface to make those prototypes usable and shareable.

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What is Streamlit?

Streamlit is a Python library that transforms scripts into interactive web apps. Its appeal lies in simplicity:

• Minimal code: A few lines can create buttons, sliders, text inputs, or file uploaders.

• No front-end skills required: You don’t need to know HTML, CSS, or JavaScript.

• Instant sharing: Apps can be deployed on Streamlit Community Cloud or integrated into platforms like Snowflake.

For data scientists, researchers, and AI developers, this means you can focus on logic and models while still delivering polished, interactive demos.

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The Prototyping Workflow

The course outlines a practical workflow for building GenAI apps with Streamlit. Here’s a simplified version:

1. Start Small
   Begin with a minimal app—often a chatbot powered by an LLM. The goal is to get something working quickly, not to perfect it.

2. Layer in Prompt Engineering
   Improve the quality of responses by refining prompts. Streamlit makes it easy to expose prompt variations through text boxes or dropdowns, so you can experiment interactively.

3. Add Retrieval-Augmented Generation (RAG)
   Connect your app to external data sources. For example, you might let the chatbot answer questions based on a company’s knowledge base or a dataset stored in Snowflake.

4. Deploy for Feedback
   Push the prototype to Streamlit Community Cloud or Snowflake. Share the link with colleagues or users and gather feedback.

5. Iterate
   Use the feedback to refine prompts, improve data connections, or adjust the interface. Because Streamlit apps are lightweight, iteration cycles are fast.

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Example: A Simple Chatbot

Here’s a minimal Streamlit app that connects to an LLM (using OpenAI as an example):

import streamlit as st
import openai

# Set your OpenAI API key
openai.api_key = st.secrets[”OPENAI_API_KEY”]

st.title(”Quick Chatbot Prototype”)

# Initialize chat history
if “messages” not in st.session_state:
    st.session_state.messages = []

user_input = st.text_input(”Ask me anything:”)

if user_input:
    # Append user message to chat history
    st.session_state.messages.append({”role”: “user”, “content”: user_input})

    # Call OpenAI ChatCompletion with the full conversation history to maintain context
    response = openai.ChatCompletion.create(
        model=”gpt-3.5-turbo”,
        messages=st.session_state.messages
    )
    
    answer = response[”choices”][0][”message”][”content”]
    
    # Append assistant’s response to chat history
    st.session_state.messages.append({”role”: “assistant”, “content”: answer})
    
    # Display the assistant’s response
    st.write(answer)

This script:

• Creates a text input box.

• Sends the input to an LLM.

• Displays the response.

It’s only a few lines of code, but it produces a working chatbot you can share.

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Adding Prompt Engineering

Prompt engineering is about shaping the model’s behavior. With Streamlit, you can expose prompt templates as editable fields:

import streamlit as st
import openai

# Set OpenAI API key securely
openai.api_key = st.secrets[”OPENAI_API_KEY”]

prompt_template = st.text_area(
    “Prompt template:”,
    “You are a helpful assistant. Answer clearly and concisely.\n\nUser: {question}\nAssistant:”
)

user_input = st.text_input(”Ask me anything:”)

if user_input:
    prompt = prompt_template.format(question=user_input)
    response = openai.Completion.create(
        model=”text-davinci-003”,
        prompt=prompt,
        max_tokens=200
    )
    st.write(response[”choices”][0][”text”])

Now you can experiment with different instructions without changing the code.

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Adding RAG (Retrieval-Augmented Generation)

RAG combines LLMs with external data. For example, you might let the chatbot answer based on a set of documents. A simplified version looks like this:

import streamlit as st
import openai

openai.api_key = st.secrets[”OPENAI_API_KEY”]

prompt_template = st.text_area(
    “Prompt template:”,
    “You are a helpful assistant. Answer clearly and concisely.\n\nUser: {question}\nAssistant:”
)

user_input = st.text_input(”Ask me anything:”)

if user_input:
    prompt = prompt_template.format(question=user_input)
    response = openai.Completion.create(
        model=”text-davinci-003”,
        prompt=prompt,
        max_tokens=200
    )
    st.write(response[”choices”][0][”text”])

This setup allows the chatbot to ground its answers in your own dataset, making it more useful for specific domains.

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Deployment

When building a prototype, keep things simple and focus on the main interaction—don’t add unnecessary features. Make it easy for users to adjust prompts, settings, or data inputs. Share your work early and improve it quickly through feedback. If your prototype handles sensitive data, make sure it runs in a secure environment.

Once your prototype works locally, you can deploy it:

• Streamlit Community Cloud: Free and simple for sharing demos.

• Snowflake + Streamlit: For secure, production-ready environments with enterprise data.

Deployment is as simple as pushing your code to GitHub and linking it to Streamlit Cloud.

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Wrapping Up

Streamlit doesn’t replace production systems, but it gives you a powerful way to explore, test, and communicate ideas. In the fast-moving world of generative AI, that speed of exploration is often the difference between leading and lagging.

Rapid prototyping with Streamlit is less about building polished products and more about accelerating learning. By lowering the barrier to creating interactive GenAI apps, it allows individuals and teams to validate ideas quickly, gather feedback, and refine direction.

In practice, this means fewer stalled discussions and more tangible progress. Whether you’re experimenting with a chatbot, a summarizer, or a data assistant, the workflow is the same: start small, iterate fast, and share early.