Practice: Rapid prototyping, POCs and pilots with measurable success criteria
Generated by Claude through GigAI 0.1.11.10 on 2026-10-11.
Main tool: Streamlit · Streamlit turns a plain Python script into a clickable app in minutes, so the same file can hold the prototype, its written success criteria and the pilot's evaluation results. · Alternatives: Gradio is quicker for a single model input and output demo but weaker for multi-section dashboards; Jupyter suits private exploration but is not something a customer can click through; a custom web frontend on a hosting platform gives full control over the interface but costs far more build time; Langfuse, LangSmith and Braintrust are evaluation and tracing backends that complement rather than replace a demo interface.
Every step links into the tool's own documentation; links were checked on 2026-10-11 · version 1
Setup (one time)
1. Check that a supported Python version and a package installer are on the laptop, then create a project folder with a fresh virtual environment.
An isolated environment keeps the prototype reproducible and makes it easy to hand over to a customer engineer later.
Explained in: Install Streamlit - Streamlit Docs (docs.streamlit.io), checked 2026-10-11
Done when: The virtual environment is active in the terminal and the project folder is otherwise empty.
2. Install Streamlit into the environment using the command line option and start the built-in hello app.
The hello app confirms the install and the local server work before any of your own code is involved.
Explained in: Install Streamlit - Streamlit Docs (docs.streamlit.io), checked 2026-10-11
Done when: The hello demo app opens in the browser on a local address.
3. Skim the get started overview to see how installation, fundamentals and first apps fit together.
Knowing the map of the documentation saves time when a prototype has only a few days of appetite.
Explained in: Get started with Streamlit - Streamlit Docs (docs.streamlit.io), checked 2026-10-11
Done when: You can name the three parts of the get started section and where the fundamentals live.
Steps
1. Run a first app of your own: create one script that shows a title and a small table, and start it with the streamlit run command.
This is the whole build loop for a prototype: edit a script, save, see the app update. Getting it under a minute is what makes fast prototyping possible.
Explained in: First steps building Streamlit apps - Streamlit Docs (docs.streamlit.io), checked 2026-10-11
Done when: Your own script renders in the browser and updates when you save an edit.
2. Create a slider and a text input in the script and watch the whole script rerun on each interaction.
The top to bottom rerun on every interaction is the core execution model, and every later design choice depends on understanding it.
Explained in: Fundamental concepts - Streamlit Docs (docs.streamlit.io), checked 2026-10-11
Done when: A counter or timestamp printed in the app changes every time you move the slider.
3. Build the prototype fast by following one tutorial to a working chat or form app, with the model call replaced by a stub function so no paid API is needed.
Starting from a tutorial pattern gets a clickable demo in front of the customer in hours. A stubbed response keeps it laptop-only while the interface and flow are tested.
Explained in: Tutorials - Streamlit Docs (docs.streamlit.io), checked 2026-10-11
Done when: You can type an input and the app shows a stubbed answer in the page.
4. Configure session state and caching so the conversation history survives reruns and the stubbed call is not repeated for the same input.
Without state the prototype forgets everything on each click, and without caching a real model call would be paid for again on every rerun.
Explained in: Development concepts - Streamlit Docs (docs.streamlit.io), checked 2026-10-11
Done when: Earlier inputs stay visible after new interactions and a repeated input returns instantly.
5. Write a header block at the top of the app that states whether this is a prototype, a POC or a pilot and the one question it is meant to answer, using the text elements.
A prototype proves the idea is worth pursuing, a POC proves it is feasible on the customer's data, and a pilot proves it holds up with real users. Putting the stage on screen stops the audience expecting more than the stage proves.
Explained in: API Reference - Streamlit Docs (docs.streamlit.io), checked 2026-10-11
Done when: The first thing visible in the app is the stage name and a single question in one sentence.
6. Write an appetite and scope panel with a caption giving the time box and end date, the one workflow that is in scope and a short list of what is out of scope.
Fixing the appetite before building forces the problem to be narrowed to fit the time, not the time stretched to fit the problem.
Explained in: API Reference - Streamlit Docs (docs.streamlit.io), checked 2026-10-11
Done when: The app shows an end date, one in-scope workflow and at least three explicit out-of-scope items.
7. Write the success criteria as a row of metric elements, each with a current value, a target threshold and a delta against that target.
A criterion is measurable only when it has a number and a pass line agreed before the build. Metrics make the go or no-go evidence readable at a glance.
Explained in: API Reference - Streamlit Docs (docs.streamlit.io), checked 2026-10-11
Done when: Three or more metrics are displayed, each showing a value and whether it is above or below its target.
8. Load a fixed set of evaluation cases from a local file into a data editor, record pass or fail and a note per case, and save the edited table back to a dated file.
This is the evaluation loop of a pilot: the same cases, judged the same way, persisted after every change so results are evidence, not memory.
Explained in: API Reference - Streamlit Docs (docs.streamlit.io), checked 2026-10-11
Done when: A new dated results file appears on disk after you mark cases and press save.
9. Compare two saved evaluation runs side by side in a dataframe and a simple bar chart, and feed the pass rate into the metrics row.
Comparing runs shows whether a prompt or logic change actually moved the criteria. That is the signal a customer needs for the decision.
Explained in: API Reference - Streamlit Docs (docs.streamlit.io), checked 2026-10-11
Done when: Selecting two run files shows per-case differences and the metrics row updates to the newer run's pass rate.
10. Read the deployment concepts page and list what the pilot app would need to be shared beyond your laptop, using a second local browser session as the substitute for a hosted deployment.
A pilot has real users, so you need to know how dependencies, secrets and sessions behave once the app leaves your machine. No listed page gives a local serving walkthrough, so this is a reading step.
Explained in: Deployment concepts - Streamlit Docs (docs.streamlit.io), checked 2026-10-11
Done when: You have a written checklist of dependencies, secrets and access needs, and two browser sessions show independent state.
11. Reproduce the app from scratch: declare pinned dependencies in a requirements file, create a fresh virtual environment, install from the file and run the app again.
A pilot that only runs on the builder's laptop is not evidence. A pinned environment lets the customer or a teammate rerun the evaluation and get the same numbers.
Explained in: Installing dependencies - Streamlit Docs (docs.streamlit.io), checked 2026-10-11
Done when: The app starts in the fresh environment and the evaluation view shows the same pass rate as before.
12. Register and mount a small custom component for reviewer feedback, such as a thumbs rating with a comment, and write its returned value into the evaluation results table.
Packaging the feedback control as a registered component makes it reusable across pilots. It also moves the app toward a production-like setup where real users feed the evaluation loop directly.
Explained in: Custom components - Streamlit Docs (docs.streamlit.io), checked 2026-10-11
Done when: Submitting feedback through the mounted component adds a row to the saved results and changes the displayed metrics.
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