Showcase — Few-shot

Classify a support message into one label — billing, bug, feature_request, praise, other. A checkbox flips the few-shot examples on and off. With the examples in the prompt, the label set and the one-line format stay put. Turn them off and the model starts inventing labels and wrapping them in prose.

Run

bash bootstrap-secrets.sh              # reads ../../../../.env, writes secrets/
docker compose up --build              # default: PROVIDER=openai

Open http://localhost:3000.

To run against Gemini instead:

PROVIDER=gemini docker compose up --build

What's where

• backend/ai_openai.py — the INSTRUCTION + EXAMPLES block (the lesson) and the OpenAI call
• backend/ai_gemini.py — same prompt, Gemini call
• backend/main.py — identical FastAPI loader; reads PROVIDER and dispatches
• frontend/app/page.tsx — textarea + few-shot checkbox + result
• docker-compose.yml — two services, secrets mounted from ./secrets/

The frontend encodes <mode>\n---\n<message> (mode is zeroshot or fewshot) into the single-string input, keeping the def run(input: str) -> str contract identical across the week's showcases.

Stop

docker compose down

unzip few-shot.zip
cd few-shot
bash bootstrap-secrets.sh   # one-time: pulls API keys into ./secrets
docker compose up --build   # default provider: openai
# or:  PROVIDER=gemini docker compose up --build

Los mismos módulos que ejecuta el botón Run. El proyecto completo (frontend, Dockerfile, compose) está en el ZIP, pestaña README.

backend/ai_openai.py

"""Week 2 - Showcase 2 (OpenAI): zero-shot vs few-shot, side by side.

Same classification task, same instruction. The only thing the toggle
changes is whether a handful of worked examples ride along in the prompt.
Zero-shot drifts on the label set and the format; few-shot locks both.
The frontend encodes "<mode>\\n---\\n<text>" where mode is zeroshot|fewshot.
"""
from openai import OpenAI

_client = OpenAI()

LABELS = "billing, bug, feature_request, praise, other"

INSTRUCTION = (
    "Classify the support message into exactly one of these labels: "
    f"{LABELS}. Respond with only `label: <one label>` and nothing else."
)

# The few-shot block: three worked examples that pin the label vocabulary
# and the output line. This is the entire difference the toggle makes.
EXAMPLES = """
Message: My card got charged twice this month.
label: billing

Message: The export button does nothing when I click it.
label: bug

Message: Any chance you could add dark mode?
label: feature_request
""".strip()


def run(payload: str) -> str:
    mode, _, text = payload.partition("\n---\n")
    mode = (mode or "fewshot").strip()
    text = (text or payload).strip()

    if mode == "zeroshot":
        prompt = f"{INSTRUCTION}\n\nMessage: {text}"
    else:
        prompt = f"{INSTRUCTION}\n\n{EXAMPLES}\n\nMessage: {text}"

    response = _client.responses.create(
        model="gpt-5.4-nano", input=prompt, max_output_tokens=20,
    )
    return response.output_text

backend/ai_gemini.py

"""Week 2 - Showcase 2 (Gemini): zero-shot vs few-shot, side by side."""
import os

from google import genai
from google.genai import types

_client = genai.Client(api_key=os.environ["GEMINI_API_KEY"])

LABELS = "billing, bug, feature_request, praise, other"

INSTRUCTION = (
    "Classify the support message into exactly one of these labels: "
    f"{LABELS}. Respond with only `label: <one label>` and nothing else."
)

EXAMPLES = """
Message: My card got charged twice this month.
label: billing

Message: The export button does nothing when I click it.
label: bug

Message: Any chance you could add dark mode?
label: feature_request
""".strip()


def run(payload: str) -> str:
    mode, _, text = payload.partition("\n---\n")
    mode = (mode or "fewshot").strip()
    text = (text or payload).strip()

    if mode == "zeroshot":
        prompt = f"{INSTRUCTION}\n\nMessage: {text}"
    else:
        prompt = f"{INSTRUCTION}\n\n{EXAMPLES}\n\nMessage: {text}"

    response = _client.models.generate_content(
        model="gemini-3.1-flash-lite",
        contents=prompt,
        config=types.GenerateContentConfig(max_output_tokens=20),
    )
    return response.text

Archivos del proyecto

• .gitignore
• README.md
• backend/Dockerfile
• backend/ai_gemini.py
• backend/ai_openai.py
• backend/main.py
• backend/requirements.txt
• bootstrap-secrets.sh
• docker-compose.yml
• frontend/Dockerfile
• frontend/app/layout.tsx
• frontend/app/page.tsx
• frontend/next.config.ts
• frontend/package.json
• frontend/tsconfig.json